ICA if fMRI based on a convolutive mixture model

DEFF Research Database (Denmark)

Hansen, Lars Kai

2003-01-01

processing strategies. Global linear dependencies can be probed by independent component analysis (ICA) based on higher order statistics or spatio-temporal properties. With ICA we separate the different sources of the fMRI signal. ICA can be performed assuming either spatial or temporal independency. A major...... of the response images (left to right, starting with zero lag in the upper left corner) shows the characteristic quick response build up, followed by a negative undershoot which is visible towards the end of the image sequence....

On the Link Between L1-PCA and ICA.

Science.gov (United States)

Martin-Clemente, Ruben; Zarzoso, Vicente

2017-03-01

Principal component analysis (PCA) based on L1-norm maximization is an emerging technique that has drawn growing interest in the signal processing and machine learning research communities, especially due to its robustness to outliers. The present work proves that L1-norm PCA can perform independent component analysis (ICA) under the whitening assumption. However, when the source probability distributions fulfil certain conditions, the L1-norm criterion needs to be minimized rather than maximized, which can be accomplished by simple modifications on existing optimal algorithms for L1-PCA. If the sources have symmetric distributions, we show in addition that L1-PCA is linked to kurtosis optimization. A number of numerical experiments illustrate the theoretical results and analyze the comparative performance of different algorithms for ICA via L1-PCA. Although our analysis is asymptotic in the sample size, this equivalence opens interesting new perspectives for performing ICA using optimal algorithms for L1-PCA with guaranteed global convergence while inheriting the increased robustness to outliers of the L1-norm criterion.

Determination of the optimal number of components in independent components analysis.

Science.gov (United States)

Kassouf, Amine; Jouan-Rimbaud Bouveresse, Delphine; Rutledge, Douglas N

2018-03-01

Independent components analysis (ICA) may be considered as one of the most established blind source separation techniques for the treatment of complex data sets in analytical chemistry. Like other similar methods, the determination of the optimal number of latent variables, in this case, independent components (ICs), is a crucial step before any modeling. Therefore, validation methods are required in order to decide about the optimal number of ICs to be used in the computation of the final model. In this paper, three new validation methods are formally presented. The first one, called Random_ICA, is a generalization of the ICA_by_blocks method. Its specificity resides in the random way of splitting the initial data matrix into two blocks, and then repeating this procedure several times, giving a broader perspective for the selection of the optimal number of ICs. The second method, called KMO_ICA_Residuals is based on the computation of the Kaiser-Meyer-Olkin (KMO) index of the transposed residual matrices obtained after progressive extraction of ICs. The third method, called ICA_corr_y, helps to select the optimal number of ICs by computing the correlations between calculated proportions and known physico-chemical information about samples, generally concentrations, or between a source signal known to be present in the mixture and the signals extracted by ICA. These three methods were tested using varied simulated and experimental data sets and compared, when necessary, to ICA_by_blocks. Results were relevant and in line with expected ones, proving the reliability of the three proposed methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA.

Science.gov (United States)

Zhou, Weidong; Gotman, Jean

2004-01-01

In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and superGaussian sources. A pre-whitening procedure is performed to de-correlate the mixed signals before extracting sources. The experimental results indicate the electromyogram (EMG) and electrocardiograph (ECG) artifacts in electroencephalograph (EEG) can be removed by a combination of wavelet threshold de-noising and ICA.

Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

Directory of Open Access Journals (Sweden)

David Overbye

2005-06-01

Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

Science.gov (United States)

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Independent component analysis based filtering for penumbral imaging

International Nuclear Information System (INIS)

Chen Yenwei; Han Xianhua; Nozaki, Shinya

2004-01-01

We propose a filtering based on independent component analysis (ICA) for Poisson noise reduction. In the proposed filtering, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (shrinkage). The proposed filter, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simulation results and experimental results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters

High dimensional ICA analysis detects within-network functional connectivity damage of default mode and sensory motor networks in Alzheimer's disease

Directory of Open Access Journals (Sweden)

Ottavia eDipasquale

2015-02-01

Full Text Available High dimensional independent component analysis (ICA, compared to low dimensional ICA, allows performing a detailed parcellation of the resting state networks. The purpose of this study was to give further insight into functional connectivity (FC in Alzheimer’s disease (AD using high dimensional ICA. For this reason, we performed both low and high dimensional ICA analyses of resting state fMRI (rfMRI data of 20 healthy controls and 21 AD patients, focusing on the primarily altered default mode network (DMN and exploring the sensory motor network (SMN. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting state sub-networks. Due to the higher sensitivity of the high dimensional ICA analysis, our results suggest that high-dimensional decomposition in sub-networks is very promising to better localize FC alterations in AD and that FC damage is not confined to the default mode network.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

Energy Technology Data Exchange (ETDEWEB)

Araujo, Draulio B de [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Barros, Allan Kardec [Department of Electrical Engineering, Federal University of Maranhao, Sao Luis, Maranhao (Brazil); Estombelo-Montesco, Carlos [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Zhao, Hui [Department of Medical Physics, University of Wisconsin, Madison, WI (United States); Filho, A C Roque da Silva [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Baffa, Oswaldo [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Wakai, Ronald [Department of Medical Physics, University of Wisconsin, Madison, WI (United States); Ohnishi, Noboru [Department of Information Engineering, Nagoya University (Japan)

2005-10-07

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Bridge Diagnosis by Using Nonlinear Independent Component Analysis and Displacement Analysis

Science.gov (United States)

Zheng, Juanqing; Yeh, Yichun; Ogai, Harutoshi

A daily diagnosis system for bridge monitoring and maintenance is developed based on wireless sensors, signal processing, structure analysis, and displacement analysis. The vibration acceleration data of a bridge are firstly collected through the wireless sensor network by exerting. Nonlinear independent component analysis (ICA) and spectral analysis are used to extract the vibration frequencies of the bridge. After that, through a band pass filter and Simpson's rule the vibration displacement is calculated and the vibration model is obtained to diagnose the bridge. Since linear ICA algorithms work efficiently only in linear mixing environments, a nonlinear ICA model, which is more complicated, is more practical for bridge diagnosis systems. In this paper, we firstly use the post nonlinear method to change the signal data, after that perform linear separation by FastICA, and calculate the vibration displacement of the bridge. The processed data can be used to understand phenomena like corrosion and crack, and evaluate the health condition of the bridge. We apply this system to Nakajima Bridge in Yahata, Kitakyushu, Japan.

Independent component analysis of dynamic contrast-enhanced computed tomography images

Energy Technology Data Exchange (ETDEWEB)

Koh, T S [School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798 (Singapore); Yang, X [School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798 (Singapore); Bisdas, S [Department of Diagnostic and Interventional Radiology, Johann Wolfgang Goethe University Hospital, Theodor-Stern-Kai 7, D-60590 Frankfurt (Germany); Lim, C C T [Department of Neuroradiology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore 308433 (Singapore)

2006-10-07

Independent component analysis (ICA) was applied on dynamic contrast-enhanced computed tomography images of cerebral tumours to extract spatial component maps of the underlying vascular structures, which correspond to different haemodynamic phases as depicted by the passage of the contrast medium. The locations of arteries, veins and tumours can be separately identified on these spatial component maps. As the contrast enhancement behaviour of the cerebral tumour differs from the normal tissues, ICA yields a tumour component map that reveals the location and extent of the tumour. Tumour outlines can be generated using the tumour component maps, with relatively simple segmentation methods. (note)

An Introductory Review of Parallel Independent Component Analysis (p-ICA and a Guide to Applying p-ICA to Genetic Data and Imaging Phenotypes to Identify Disease-Associated Biological Pathways and Systems in Common Complex Disorders

Directory of Open Access Journals (Sweden)

Godfrey D Pearlson

2015-09-01

Full Text Available Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010. Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g. genome wide association (GWA. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e. simultaneous combination of SNP and neuroimage information independent component analysis (p-ICA, which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

Science.gov (United States)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

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

Science.gov (United States)

Back, A D; Weigend, A S

1997-08-01

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

A Penalized Semialgebraic Deflation ICA Algorithm for the Efficient Extraction of Interictal Epileptic Signals.

Science.gov (United States)

Becker, Hanna; Albera, Laurent; Comon, Pierre; Kachenoura, Amar; Merlet, Isabelle

2017-01-01

As a noninvasive technique, electroencephalography (EEG) is commonly used to monitor the brain signals of patients with epilepsy such as the interictal epileptic spikes. However, the recorded data are often corrupted by artifacts originating, for example, from muscle activities, which may have much higher amplitudes than the interictal epileptic signals of interest. To remove these artifacts, a number of independent component analysis (ICA) techniques were successfully applied. In this paper, we propose a new deflation ICA algorithm, called penalized semialgebraic unitary deflation (P-SAUD) algorithm, that improves upon classical ICA methods by leading to a considerably reduced computational complexity at equivalent performance. This is achieved by employing a penalized semialgebraic extraction scheme, which permits us to identify the epileptic components of interest (interictal spikes) first and obviates the need of extracting subsequent components. The proposed method is evaluated on physiologically plausible simulated EEG data and actual measurements of three patients. The results are compared to those of several popular ICA algorithms as well as second-order blind source separation methods, demonstrating that P-SAUD extracts the epileptic spikes with the same accuracy as the best ICA methods, but reduces the computational complexity by a factor of 10 for 32-channel recordings. This superior computational efficiency is of particular interest considering the increasing use of high-resolution EEG recordings, whose analysis requires algorithms with low computational cost.

Independent component analysis of normal and abnormal rhythm in twin pregnancies

International Nuclear Information System (INIS)

Mensah-Brown, Nana Aba; Lutter, William J; Wakai, Ronald T; Comani, Silvia; Strasburger, Janette F

2011-01-01

We investigated the utility of ICA for evaluation of fetal rhythm in five uncomplicated twin pregnancies and in five twin pregnancies complicated by fetal arrhythmia. Using objective and subjective criteria, we sought to determine how the signal-to-noise ratio, signal fidelity and interference rejection are affected when synthesizing the fetal signal using all the signal-containing ICA components (rank-p ICA) versus using the single dominant component (rank-1 ICA). The signal of each fetus was most commonly distributed over 1 or 2 ICA components, as previously observed in studies of singleton pregnancies; however, in 8 of 26 (31%) cases the signal of each fetus was distributed over 3, 4 or even 5 ICA components. Rank-1 ICA provided the highest SNR and interference rejection, but at the cost of reduced signal fidelity. Our results corroborate that in twin pregnancies, including twin pregnancies complicated by fetal arrhythmia, rank-1 ICA is very effective in isolating the QRS complexes of each fetus; however, it has some limitations when used for fetal rhythm evaluation due to signal distortion. Occasionally, rank-1 ICA completely separates the P-wave and the T-wave from the QRS complex, thus requiring the mixing of several ICA components to achieve acceptable signal fidelity

COMPARING INDEPENDENT COMPONENT ANALYSIS WITH PRINCIPLE COMPONENT ANALYSIS IN DETECTING ALTERATIONS OF PORPHYRY COPPER DEPOSIT (CASE STUDY: ARDESTAN AREA, CENTRAL IRAN

Directory of Open Access Journals (Sweden)

S. Mahmoudishadi

2017-09-01

Full Text Available The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA and Independent Component Analysis (ICA has been evaluated for the visible and near-infrared (VNIR and Shortwave infrared (SWIR subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6 were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

Comparing Independent Component Analysis with Principle Component Analysis in Detecting Alterations of Porphyry Copper Deposit (case Study: Ardestan Area, Central Iran)

Science.gov (United States)

Mahmoudishadi, S.; Malian, A.; Hosseinali, F.

2017-09-01

The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

Adaptive tools in virtual environments: Independent component analysis for multimedia

DEFF Research Database (Denmark)

Kolenda, Thomas

2002-01-01

The thesis investigates the role of independent component analysis in the setting of virtual environments, with the purpose of finding properties that reflect human context. A general framework for performing unsupervised classification with ICA is presented in extension to the latent semantic in...... were compared to investigate computational differences and separation results. The ICA properties were finally implemented in a chat room analysis tool and briefly investigated for visualization of search engines results....

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

Science.gov (United States)

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems

Directory of Open Access Journals (Sweden)

Zahoor Uddin

2018-01-01

Full Text Available Independent component analysis (ICA is a technique of blind source separation (BSS used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA (ASS-GAICA algorithm is presented. The proposed algorithm is free from selection of the step size parameter with improved convergence and separation performance. Different performance evaluation criteria are used to verify the effectiveness of the proposed algorithm. Performance of the proposed algorithm is compared with the FastICA and optimum block adaptive ICA (OBAICA algorithms for quasistatic and time-varying wireless channels. Simulation is performed over quadrature amplitude modulation (QAM and binary phase shift keying (BPSK signals. Results show that the proposed algorithm outperforms the FastICA and OBAICA algorithms for a wide range of signal-to-noise ratio (SNR and input data block lengths.

Tumor Classification Using High-Order Gene Expression Profiles Based on Multilinear ICA

Directory of Open Access Journals (Sweden)

Ming-gang Du

2009-01-01

Full Text Available Motivation. Independent Components Analysis (ICA maximizes the statistical independence of the representational components of a training gene expression profiles (GEP ensemble, but it cannot distinguish relations between the different factors, or different modes, and it is not available to high-order GEP Data Mining. In order to generalize ICA, we introduce Multilinear-ICA and apply it to tumor classification using high order GEP. Firstly, we introduce the basis conceptions and operations of tensor and recommend Support Vector Machine (SVM classifier and Multilinear-ICA. Secondly, the higher score genes of original high order GEP are selected by using t-statistics and tabulate tensors. Thirdly, the tensors are performed by Multilinear-ICA. Finally, the SVM is used to classify the tumor subtypes. Results. To show the validity of the proposed method, we apply it to tumor classification using high order GEP. Though we only use three datasets, the experimental results show that the method is effective and feasible. Through this survey, we hope to gain some insight into the problem of high order GEP tumor classification, in aid of further developing more effective tumor classification algorithms.

How Many Separable Sources? Model Selection In Independent Components Analysis

Science.gov (United States)

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

Science.gov (United States)

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative. PMID:22860044

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

Directory of Open Access Journals (Sweden)

Siina Pamilo

Full Text Available Independent component analysis (ICA can unravel functional brain networks from functional magnetic resonance imaging (fMRI data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren. We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

Science.gov (United States)

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

A method for independent component graph analysis of resting-state fMRI

DEFF Research Database (Denmark)

de Paula, Demetrius Ribeiro; Ziegler, Erik; Abeyasinghe, Pubuditha M.

2017-01-01

Introduction Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguou......Introduction Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non......-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Objective Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. Methods First, ICA was performed at the single-subject level in 15 healthy...... parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Results Network graph comparison between the classically constructed network...

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

Science.gov (United States)

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study

Directory of Open Access Journals (Sweden)

Robert E. Kelly

2010-01-01

Full Text Available Brain functional connectivity (FC is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA. ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-related a priori time courses, with “back-reconstruction” from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1 single-voxel, (2 few-voxel, and (3 many-voxel seed; and dual-regression-based with (4 single ICA map and (5 multiple ICA map seed.

Independent component analysis applied to long bunch beams in the Los Alamos Proton Storage Ring

Science.gov (United States)

Kolski, Jeffrey S.; Macek, Robert J.; McCrady, Rodney C.; Pang, Xiaoying

2012-11-01

Independent component analysis (ICA) is a powerful blind source separation (BSS) method. Compared to the typical BSS method, principal component analysis, ICA is more robust to noise, coupling, and nonlinearity. The conventional ICA application to turn-by-turn position data from multiple beam position monitors (BPMs) yields information about cross-BPM correlations. With this scheme, multi-BPM ICA has been used to measure the transverse betatron phase and amplitude functions, dispersion function, linear coupling, sextupole strength, and nonlinear beam dynamics. We apply ICA in a new way to slices along the bunch revealing correlations of particle motion within the beam bunch. We digitize beam signals of the long bunch at the Los Alamos Proton Storage Ring with a single device (BPM or fast current monitor) for an entire injection-extraction cycle. ICA of the digitized beam signals results in source signals, which we identify to describe varying betatron motion along the bunch, locations of transverse resonances along the bunch, measurement noise, characteristic frequencies of the digitizing oscilloscopes, and longitudinal beam structure.

Independent component analysis applied to long bunch beams in the Los Alamos Proton Storage Ring

Directory of Open Access Journals (Sweden)

Jeffrey S. Kolski

2012-11-01

Full Text Available Independent component analysis (ICA is a powerful blind source separation (BSS method. Compared to the typical BSS method, principal component analysis, ICA is more robust to noise, coupling, and nonlinearity. The conventional ICA application to turn-by-turn position data from multiple beam position monitors (BPMs yields information about cross-BPM correlations. With this scheme, multi-BPM ICA has been used to measure the transverse betatron phase and amplitude functions, dispersion function, linear coupling, sextupole strength, and nonlinear beam dynamics. We apply ICA in a new way to slices along the bunch revealing correlations of particle motion within the beam bunch. We digitize beam signals of the long bunch at the Los Alamos Proton Storage Ring with a single device (BPM or fast current monitor for an entire injection-extraction cycle. ICA of the digitized beam signals results in source signals, which we identify to describe varying betatron motion along the bunch, locations of transverse resonances along the bunch, measurement noise, characteristic frequencies of the digitizing oscilloscopes, and longitudinal beam structure.

Independent component analysis based digital signal processing in coherent optical fiber communication systems

Science.gov (United States)

Li, Xiang; Luo, Ming; Qiu, Ying; Alphones, Arokiaswami; Zhong, Wen-De; Yu, Changyuan; Yang, Qi

2018-02-01

In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed.

Group-ICA model order highlights patterns of functional brain connectivity

Directory of Open Access Journals (Sweden)

Ahmed eAbou Elseoud

2011-06-01

Full Text Available Resting-state networks (RSNs can be reliably and reproducibly detected using independent component analysis (ICA at both individual subject and group levels. Altering ICA dimensionality (model order estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual-regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

Science.gov (United States)

Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

2010-12-01

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

Gene Module Identification from Microarray Data Using Nonnegative Independent Component Analysis

Directory of Open Access Journals (Sweden)

Ting Gong

2007-01-01

Full Text Available Genes mostly interact with each other to form transcriptional modules for performing single or multiple functions. It is important to unravel such transcriptional modules and to determine how disturbances in them may lead to disease. Here, we propose a non-negative independent component analysis (nICA approach for transcriptional module discovery. nICA method utilizes the non-negativity constraint to enforce the independence of biological processes within the participated genes. In such, nICA decomposes the observed gene expression into positive independent components, which fi ts better to the reality of corresponding putative biological processes. In conjunction with nICA modeling, visual statistical data analyzer (VISDA is applied to group genes into modules in latent variable space. We demonstrate the usefulness of the approach through the identification of composite modules from yeast data and the discovery of pathway modules in muscle regeneration.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

Science.gov (United States)

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (pcoding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (pcoding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

Independent component analysis reveals new and biologically significant structures in micro array data

Directory of Open Access Journals (Sweden)

Veerla Srinivas

2006-06-01

Full Text Available Abstract Background An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes. Results We applied independent component analysis (ICA to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology (GO categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA.

ICAS-PAT: A Software for Design, Analysis and Validation of PAT Systems

DEFF Research Database (Denmark)

Singh, Ravendra; Gernaey, Krist; Gani, Rafiqul

2010-01-01

end product qualities. In an earlier article, Singh et al. [Singh, R., Gernaey, K. V., Gani, R. (2009). Model-based computer-aided framework for design of process monitoring and analysis systems. Computers & Chemical Engineering, 33, 22–42] proposed the use of a systematic model and data based...... methodology to design appropriate PAT systems. This methodology has now been implemented into a systematic computer-aided framework to develop a software (ICAS-PAT) for design, validation and analysis of PAT systems. Two supporting tools needed by ICAS-PAT have also been developed: a knowledge base...... (consisting of process knowledge as well as knowledge on measurement methods and tools) and a generic model library (consisting of process operational models). Through a tablet manufacturing process example, the application of ICAS-PAT is illustrated, highlighting as well, the main features of the software....

Independent component analysis for understanding multimedia content

DEFF Research Database (Denmark)

Kolenda, Thomas; Hansen, Lars Kai; Larsen, Jan

2002-01-01

Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval...

Secure Communication Based on a Hybrid of Chaos and Ica Encryptions

Science.gov (United States)

Chen, Wei Ching; Yuan, John

Chaos and independent component analysis (ICA) encryptions are two novel schemes for secure communications. In this paper, a new scheme combining chaos and ICA techniques is proposed to enhance the security level during communication. In this scheme, a master chaotic system is embedded at the transmitter. The message signal is mixed with a chaotic signal and a Gaussian white noise into two mixed signals and then transmitted to the receiver through the public channels. A signal for synchronization is transmitted through another public channel to the receiver where a slave chaotic system is embedded to reproduce the chaotic signal. A modified ICA is used to recover the message signal at the receiver. Since only two of the three transmitted signals contain the information of message signal, a hacker would not be able to retrieve the message signal by using ICA even though all the transmitted signals are intercepted. Spectrum analyses are used to prove that the message signal can be securely hidden under this scheme.

Convolutive ICA for Spatio-Temporal Analysis of EEG

DEFF Research Database (Denmark)

Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

2007-01-01

in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

Science.gov (United States)

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

News Schemes for Activity Recognition Systems Using PCA-WSVM, ICA-WSVM, and LDA-WSVM

Directory of Open Access Journals (Sweden)

M’hamed Bilal Abidine

2015-08-01

Full Text Available Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA, Independent Component Analysis (ICA, and Linear Discriminant Analysis (LDA. The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM classifier. This classifier is used to handle the problem of imbalanced activity data from the sensor readings. The experiments were implemented on multiple real-world datasets with Conditional Random Fields (CRF, standard Support Vector Machines (SVM, Weighted SVM, and combined methods PCA+WSVM, ICA+WSVM, and LDA+WSVM showed that LDA+WSVM had a higher recognition rate than other methods for activity recognition.

Applying independent component analysis to clinical fMRI at 7 T

OpenAIRE

Simon Daniel Robinson; Veronika eSchöpf; Pedro eCardoso; Alexander eGeissler; Alexander eGeissler; Florian Ph.S Fischmeister; Florian Ph.S Fischmeister; Moritz eWurnig; Moritz eWurnig; Siegfried eTrattnig; Roland eBeisteiner; Roland eBeisteiner

2013-01-01

Increased BOLD sensitivity at 7 T offers the possibility to increase the reliability of fMRI, but ultra-high field is also associated with an increase in artifacts related to head motion, Nyquist ghosting and parallel imaging reconstruction errors. In this study, the ability of Independent Component Analysis (ICA) to separate activation from these artifacts was assessed in a 7 T study of neurological patients performing chin and hand motor tasks. ICA was able to isolate primary motor activati...

Independent component analysis applied to pulse oximetry in the estimation of the arterial oxygen saturation (SpO2) - a comparative study

DEFF Research Database (Denmark)

Jensen, Thomas; Duun, Sune Bro; Larsen, Jan

2009-01-01

We examine various independent component analysis (ICA) digital signal processing algorithms for estimating the arterial oxygen saturation (SpO2) as measured by a reflective pulse oximeter. The ICA algorithms examined are FastICA, Maximum Likelihood ICA (ICAML), Molgedey and Schuster ICA (ICAMS......), and Mean Field ICA (ICAMF). The signal processing includes pre-processing bandpass filtering to eliminate noise, and post-processing by calculating the SpO2. The algorithms are compared to the commercial state-of-the-art algorithm Discrete Saturation Transform (DST) by Masimo Corporation...

Independent component analysis classification of laser induced breakdown spectroscopy spectra

International Nuclear Information System (INIS)

Forni, Olivier; Maurice, Sylvestre; Gasnault, Olivier; Wiens, Roger C.; Cousin, Agnès; Clegg, Samuel M.; Sirven, Jean-Baptiste; Lasue, Jérémie

2013-01-01

The ChemCam instrument on board Mars Science Laboratory (MSL) rover uses the laser-induced breakdown spectroscopy (LIBS) technique to remotely analyze Martian rocks. It retrieves spectra up to a distance of seven meters to quantify and to quantitatively analyze the sampled rocks. Like any field application, on-site measurements by LIBS are altered by diverse matrix effects which induce signal variations that are specific to the nature of the sample. Qualitative aspects remain to be studied, particularly LIBS sample identification to determine which samples are of interest for further analysis by ChemCam and other rover instruments. This can be performed with the help of different chemometric methods that model the spectra variance in order to identify a the rock from its spectrum. In this paper we test independent components analysis (ICA) rock classification by remote LIBS. We show that using measures of distance in ICA space, namely the Manhattan and the Mahalanobis distance, we can efficiently classify spectra of an unknown rock. The Mahalanobis distance gives overall better performances and is easier to manage than the Manhattan distance for which the determination of the cut-off distance is not easy. However these two techniques are complementary and their analytical performances will improve with time during MSL operations as the quantity of available Martian spectra will grow. The analysis accuracy and performances will benefit from a combination of the two approaches. - Highlights: • We use a novel independent component analysis method to classify LIBS spectra. • We demonstrate the usefulness of ICA. • We report the performances of the ICA classification. • We compare it to other classical classification schemes

Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults.

Science.gov (United States)

Ponomarev, Valery A; Mueller, Andreas; Candrian, Gian; Grin-Yatsenko, Vera A; Kropotov, Juri D

2014-01-01

To investigate the performance of the spectral analysis of resting EEG, Current Source Density (CSD) and group independent components (gIC) in diagnosing ADHD adults. Power spectra of resting EEG, CSD and gIC (19 channels, linked ears reference, eyes open/closed) from 96 ADHD and 376 healthy adults were compared between eyes open and eyes closed conditions, and between groups of subjects. Pattern of differences in gIC and CSD spectral power between conditions was approximately similar, whereas it was more widely spatially distributed for EEG. Size effect (Cohen's d) of differences in gIC and CSD spectral power between groups of subjects was considerably greater than in the case of EEG. Significant reduction of gIC and CSD spectral power depending on conditions was found in ADHD patients. Reducing power in a wide frequency range in the fronto-central areas is a common phenomenon regardless of whether the eyes were open or closed. Spectral power of local EEG activity isolated by gICA or CSD in the fronto-central areas may be a suitable marker for discrimination of ADHD and healthy adults. Spectral analysis of gIC and CSD provides better sensitivity to discriminate ADHD and healthy adults. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

Science.gov (United States)

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

Applying independent component analysis to clinical FMRI at 7 t

OpenAIRE

Robinson, Simon Daniel; Schöpf, Veronika; Cardoso, Pedro; Geissler, Alexander; Fischmeister, Florian P S; Wurnig, Moritz; Trattnig, Siegfried; Beisteiner, Roland

2013-01-01

Increased BOLD sensitivity at 7 T offers the possibility to increase the reliability of fMRI, but ultra-high field is also associated with an increase in artifacts related to head motion, Nyquist ghosting, and parallel imaging reconstruction errors. In this study, the ability of independent component analysis (ICA) to separate activation from these artifacts was assessed in a 7 T study of neurological patients performing chin and hand motor tasks. ICA was able to isolate primary motor activat...

Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

Science.gov (United States)

Zhang, Sheng; Li, Chiang-Shan R

2017-11-01

As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

Science.gov (United States)

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

Science.gov (United States)

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

Directory of Open Access Journals (Sweden)

Wensheng Dai

2014-01-01

Full Text Available Sales forecasting is one of the most important issues in managing information technology (IT chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR, is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA, temporal ICA (tICA, and spatiotemporal ICA (stICA to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

Science.gov (United States)

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Neural spike sorting using iterative ICA and a deflation-based approach.

Science.gov (United States)

Tiganj, Z; Mboup, M

2012-12-01

We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

How Many Separable Sources? Model Selection In Independent Components Analysis

DEFF Research Database (Denmark)

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....

Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package

Directory of Open Access Journals (Sweden)

Pierre Lafaye de Micheaux

2011-10-01

Full Text Available For statistical analysis of functional magnetic resonance imaging (fMRI data sets, we propose a data-driven approach based on independent component analysis (ICA implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.

Blind Separation of Acoustic Signals Combining SIMO-Model-Based Independent Component Analysis and Binary Masking

Directory of Open Access Journals (Sweden)

Hiekata Takashi

2006-01-01

Full Text Available A new two-stage blind source separation (BSS method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO-model-based independent component analysis (ICA and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, our novel SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by the proposed method compared with that achieved by conventional BSS methods. In addition, the real-time implementation of the proposed BSS is illustrated.

Genetic algorithm using independent component analysis in x-ray reflectivity curve fitting of periodic layer structures

International Nuclear Information System (INIS)

Tiilikainen, J; Bosund, V; Tilli, J-M; Sormunen, J; Mattila, M; Hakkarainen, T; Lipsanen, H

2007-01-01

A novel genetic algorithm (GA) utilizing independent component analysis (ICA) was developed for x-ray reflectivity (XRR) curve fitting. EFICA was used to reduce mutual information, or interparameter dependences, during the combinatorial phase. The performance of the new algorithm was studied by fitting trial XRR curves to target curves which were computed using realistic multilayer models. The median convergence properties of conventional GA, GA using principal component analysis and the novel GA were compared. GA using ICA was found to outperform the other methods with problems having 41 parameters or more to be fitted without additional XRR curve calculations. The computational complexity of the conventional methods was linear but the novel method had a quadratic computational complexity due to the applied ICA method which sets a practical limit for the dimensionality of the problem to be solved. However, the novel algorithm had the best capability to extend the fitting analysis based on Parratt's formalism to multiperiodic layer structures

Use of sEMG in identification of low level muscle activities: features based on ICA and fractal dimension.

Science.gov (United States)

Naik, Ganesh R; Kumar, Dinesh K; Arjunan, Sridhar

2009-01-01

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.

Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers.

Science.gov (United States)

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-04-15

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original

Diet of the grass lizard Microlophus thoracicus icae in the Ica river valley, Peru

Directory of Open Access Journals (Sweden)

José Pérez Z.

2015-10-01

Full Text Available The diet of grass lizard, Microlophus thoracicus icae, was evaluated in three localities of the Ica River Valley, Peru. The dietary pattern was characterized by high consumption of vegetable material, mainly Prosopis spp. leaves, and invertebrates as ants and insect larvae. No significant relationships were found between body size, number of prey eaten or volume consumed. The juvenile, male and female M. t. icae not showed significant differences regarding number of ants or insect larvae consumed, neither on the proportion consumed of plant material. However, total volume of plant material was different between males and females, compared to juveniles. Multivariate analysis showed no evident difference in the diets of juveniles, males and females. Trophic niche amplitude for M. t. icae was Bij = 6.97. The consumption of plant material and invertebrates is important for both juvenile and adult iguanas, therefore; no clear age difference in diet was observed in the individuals studied. This species would present great diet plasticity (omnivory influenced by the local variation of food resources. Possible consequences of a varied diet may include particular characteristics of its parasites, foraging strategies and efficiency, thermoregulation, morphology, among others.

Estimation of compound distribution in spectral images of tomatoes using independent component analysis

NARCIS (Netherlands)

Polder, G.; Heijden, van der G.W.A.M.

2003-01-01

Independent Component Analysis (ICA) is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components

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

Science.gov (United States)

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

2014-12-01

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

Electrically evoked compound action potentials artefact rejection by independent component analysis: procedure automation.

Science.gov (United States)

Akhoun, Idrick; McKay, Colette; El-Deredy, Wael

2015-01-15

Independent-components-analysis (ICA) successfully separated electrically-evoked compound action potentials (ECAPs) from the stimulation artefact and noise (ECAP-ICA, Akhoun et al., 2013). This paper shows how to automate the ECAP-ICA artefact cancellation process. Raw-ECAPs without artefact rejection were consecutively recorded for each stimulation condition from at least 8 intra-cochlear electrodes. Firstly, amplifier-saturated recordings were discarded, and the data from different stimulus conditions (different current-levels) were concatenated temporally. The key aspect of the automation procedure was the sequential deductive source categorisation after ICA was applied with a restriction to 4 sources. The stereotypical aspect of the 4 sources enables their automatic classification as two artefact components, a noise and the sought ECAP based on theoretical and empirical considerations. The automatic procedure was tested using 8 cochlear implant (CI) users and one to four stimulus electrodes. The artefact and noise sources were successively identified and discarded, leaving the ECAP as the remaining source. The automated ECAP-ICA procedure successfully extracted the correct ECAPs compared to standard clinical forward masking paradigm in 22 out of 26 cases. ECAP-ICA does not require extracting the ECAP from a combination of distinct buffers as it is the case with regular methods. It is an alternative that does not have the possible bias of traditional artefact rejections such as alternate-polarity or forward-masking paradigms. The ECAP-ICA procedure bears clinical relevance, for example as the artefact rejection sub-module of automated ECAP-threshold detection techniques, which are common features of CI clinical fitting software. Copyright © 2014. Published by Elsevier B.V.

Determination of arterial input function in dynamic susceptibility contrast MRI using group independent component analysis technique

International Nuclear Information System (INIS)

Chen, S.; Liu, H.-L.; Yang Yihong; Hsu, Y.-Y.; Chuang, K.-S.

2006-01-01

Quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) requires the determination of the arterial input function (AIF). The segmentation of surrounding tissue by manual selection is error-prone due to the partial volume artifacts. Independent component analysis (ICA) has the advantage in automatically decomposing the signals into interpretable components. Recently group ICA technique has been applied to fMRI study and showed reduced variance caused by motion artifact and noise. In this work, we investigated the feasibility and efficacy of the use of group ICA technique to extract the AIF. Both simulated and in vivo data were analyzed in this study. The simulation data of eight phantoms were generated using randomized lesion locations and time activity curves. The clinical data were obtained from spin-echo EPI MR scans performed in seven normal subjects. Group ICA technique was applied to analyze data through concatenating across seven subjects. The AIFs were calculated from the weighted average of the signals in the region selected by ICA. Preliminary results of this study showed that group ICA technique could not extract accurate AIF information from regions around the vessel. The mismatched location of vessels within the group reduced the benefits of group study

Impact of calibration errors on CMB component separation using FastICA and ILC

Science.gov (United States)

Dick, Jason; Remazeilles, Mathieu; Delabrouille, Jacques

2010-01-01

The separation of emissions from different astrophysical processes is an important step towards the understanding of observational data. This topic of component separation is of particular importance in the observation of the relic cosmic microwave background (CMB) radiation, as performed by the Wilkinson Microwave Anisotropy Probe satellite and the more recent Planck mission, launched on 2009 May 14 from Kourou and currently taking data. When performing any sort of component separation, some assumptions about the components must be used. One assumption that many techniques typically use is knowledge of the frequency scaling of one or more components. This assumption may be broken in the presence of calibration errors. Here we compare, in the context of imperfect calibration, the recovery of a clean map of emission of the CMB from observational data with two methods: FastICA (which makes no assumption of the frequency scaling of the components) and an `Internal Linear Combination' (ILC), which explicitly extracts a component with a given frequency scaling. We find that even in the presence of small calibration errors (less than 1 per cent) with a Planck-style mission, the ILC method can lead to inaccurate CMB reconstruction in the high signal-to-noise ratio regime, because of partial cancellation of the CMB emission in the recovered map. While there is no indication that the failure of the ILC will translate to other foreground cleaning or component separation techniques, we propose that all methods which assume knowledge of the frequency scaling of one or more components be careful to estimate the effects of calibration errors.

Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data

International Nuclear Information System (INIS)

Mantini, D; II, K E Hild; Alleva, G; Comani, S

2006-01-01

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times

Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions

Directory of Open Access Journals (Sweden)

Reinhold Orglmeister

2010-01-01

Full Text Available When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA has proven a valuable tool for this purpose. However, ICA outputs can still contain strong residual components of the interfering speakers whenever noise or reverberation is high. In such cases, nonlinear postprocessing can be applied to the ICA outputs, for the purpose of reducing remaining interferences. In order to improve robustness to the artefacts and loss of information caused by this process, recognition can be greatly enhanced by considering the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic. The aim of this paper is to show the potential to improve recognition of multiple overlapping speech signals through nonlinear postprocessing together with uncertainty-based decoding techniques.

FastICA peel-off for ECG interference removal from surface EMG.

Science.gov (United States)

Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

2016-06-13

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling.

Science.gov (United States)

Kim, Chang-Min; Park, Hyung-Min; Kim, Taesu; Choi, Yoon-Kyung; Lee, Soo-Young

2003-01-01

An field programmable gate array (FPGA) implementation of independent component analysis (ICA) algorithm is reported for blind signal separation (BSS) and adaptive noise canceling (ANC) in real time. In order to provide enormous computing power for ICA-based algorithms with multipath reverberation, a special digital processor is designed and implemented in FPGA. The chip design fully utilizes modular concept and several chips may be put together for complex applications with a large number of noise sources. Experimental results with a fabricated test board are reported for ANC only, BSS only, and simultaneous ANC/BSS, which demonstrates successful speech enhancement in real environments in real time.

The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics

Directory of Open Access Journals (Sweden)

Nicola eSoldati

2013-03-01

Full Text Available Real-time brain functional MRI (rt-fMRI allows in-vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitora target independent component (IC. We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability.

Speckle noise reduction technique for Lidar echo signal based on self-adaptive pulse-matching independent component analysis

Science.gov (United States)

Xu, Fan; Wang, Jiaxing; Zhu, Daiyin; Tu, Qi

2018-04-01

Speckle noise has always been a particularly tricky problem in improving the ranging capability and accuracy of Lidar system especially in harsh environment. Currently, effective speckle de-noising techniques are extremely scarce and should be further developed. In this study, a speckle noise reduction technique has been proposed based on independent component analysis (ICA). Since normally few changes happen in the shape of laser pulse itself, the authors employed the laser source as a reference pulse and executed the ICA decomposition to find the optimal matching position. In order to achieve the self-adaptability of algorithm, local Mean Square Error (MSE) has been defined as an appropriate criterion for investigating the iteration results. The obtained experimental results demonstrated that the self-adaptive pulse-matching ICA (PM-ICA) method could effectively decrease the speckle noise and recover the useful Lidar echo signal component with high quality. Especially, the proposed method achieves 4 dB more improvement of signal-to-noise ratio (SNR) than a traditional homomorphic wavelet method.

A short study to assess the potential of independent component analysis for motion artifact separation in wearable pulse oximeter signals.

Science.gov (United States)

Yao, Jianchu; Warren, Steve

2005-01-01

Motion artifact reduction and separation become critical when medical sensors are used in wearable monitoring scenarios. Previous research has demonstrated that independent component analysis (ICA) can be applied to pulse oximeter signals to separate photoplethysmographic (PPG) data from motion artifacts, ambient light, and other interference in low-motion environments. However, ICA assumes that all source signal component pairs are mutually independent. It is important to assess the statistical independence of the source components in PPG data, especially if ICA is to be applied in ambulatory monitoring environments, where motion artifacts can have a substantial effect on the quality of data received from light-based sensors. This paper addresses the statistical relationship between motion artifacts and PPG data by calculating the correlation coefficients between arterial volume variations and motion over a range of stationary to high-motion conditions. Analyses indicate that motion significantly affects arterial flow, so care must be taken when applying ICA to light-based sensor data acquired from wearable platforms.

Identifying constituents in commercial gasoline using Fourier transform-infrared spectroscopy and independent component analysis.

Science.gov (United States)

Pasadakis, Nikos; Kardamakis, Andreas A

2006-09-25

A new method is proposed that enables the identification of five refinery fractions present in commercial gasoline mixtures using infrared spectroscopic analysis. The data analysis and interpretation was carried out based on independent component analysis (ICA) and spectral similarity techniques. The FT-IR spectra of the gasoline constituents were determined using the ICA method, exclusively based on the spectra of their mixtures as a blind separation procedure, i.e. assuming unknown the spectra of the constituents. The identity of the constituents was subsequently determined using similarity measures commonly employed in spectra library searches against the spectra of the constituent components. The high correlation scores that were obtained in the identification of the constituents indicates that the developed method can be employed as a rapid and effective tool in quality control, fingerprinting or forensic applications, where gasoline constituents are suspected.

Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection

DEFF Research Database (Denmark)

Karlsen, Brian; Larsen, Jan; Sørensen, Helge Bjarup Dissing

2001-01-01

This paper presents statistical signal processing approaches for clutter reduction in stepped-frequency ground penetrating radar (SF-GPR) data. In particular, we suggest clutter/signal separation techniques based on principal and independent component analysis (PCA/ICA). The approaches...

Signal extraction and wave field separation in tunnel seismic prediction by independent component analysis

Science.gov (United States)

Yue, Y.; Jiang, T.; Zhou, Q.

2017-12-01

In order to ensure the rationality and the safety of tunnel excavation, the advanced geological prediction has been become an indispensable step in tunneling. However, the extraction of signal and the separation of P and S waves directly influence the accuracy of geological prediction. Generally, the raw data collected in TSP system is low quality because of the numerous disturb factors in tunnel projects, such as the power interference and machine vibration interference. It's difficult for traditional method (band-pass filtering) to remove interference effectively as well as bring little loss to signal. The power interference, machine vibration interference and the signal are original variables and x, y, z component as observation signals, each component of the representation is a linear combination of the original variables, which satisfy applicable conditions of independent component analysis (ICA). We perform finite-difference simulations of elastic wave propagation to synthetic a tunnel seismic reflection record. The method of ICA was adopted to process the three-component data, and the results show that extract the estimates of signal and the signals are highly correlated (the coefficient correlation is up to more than 0.93). In addition, the estimates of interference that separated from ICA and the interference signals are also highly correlated, and the coefficient correlation is up to more than 0.99. Thus, simulation results showed that the ICA is an ideal method for extracting high quality data from mixed signals. For the separation of P and S waves, the conventional separation techniques are based on physical characteristics of wave propagation, which require knowledge of the near-surface P and S waves velocities and density. Whereas the ICA approach is entirely based on statistical differences between P and S waves, and the statistical technique does not require a priori information. The concrete results of the wave field separation will be presented in

Evidence for anomalous network connectivity during working memory encoding in schizophrenia: an ICA based analysis.

Directory of Open Access Journals (Sweden)

Shashwath A Meda

2009-11-01

Full Text Available Numerous neuroimaging studies report abnormal regional brain activity during working memory performance in schizophrenia, but few have examined brain network integration as determined by "functional connectivity" analyses.We used independent component analysis (ICA to identify and characterize dysfunctional spatiotemporal networks in schizophrenia engaged during the different stages (encoding and recognition of a Sternberg working memory fMRI paradigm. 37 chronic schizophrenia and 54 healthy age/gender-matched participants performed a modified Sternberg Item Recognition fMRI task. Time series images preprocessed with SPM2 were analyzed using ICA. Schizophrenia patients showed relatively less engagement of several distinct "normal" encoding-related working memory networks compared to controls. These encoding networks comprised 1 left posterior parietal-left dorsal/ventrolateral prefrontal cortex, cingulate, basal ganglia, 2 right posterior parietal, right dorsolateral prefrontal cortex and 3 default mode network. In addition, the left fronto-parietal network demonstrated a load-dependent functional response during encoding. Network engagement that differed between groups during recognition comprised the posterior cingulate, cuneus and hippocampus/parahippocampus. As expected, working memory task accuracy differed between groups (p<0.0001 and was associated with degree of network engagement. Functional connectivity within all three encoding-associated functional networks correlated significantly with task accuracy, which further underscores the relevance of abnormal network integration to well-described schizophrenia working memory impairment. No network was significantly associated with task accuracy during the recognition phase.This study extends the results of numerous previous schizophrenia studies that identified isolated dysfunctional brain regions by providing evidence of disrupted schizophrenia functional connectivity using ICA within

Independent component analysis for cochlear implant artifacts attenuation from electrically evoked auditory steady-state response measurements

Science.gov (United States)

Deprez, Hanne; Gransier, Robin; Hofmann, Michael; van Wieringen, Astrid; Wouters, Jan; Moonen, Marc

2018-02-01

Objective. Electrically evoked auditory steady-state responses (EASSRs) are potentially useful for objective cochlear implant (CI) fitting and follow-up of the auditory maturation in infants and children with a CI. EASSRs are recorded in the electro-encephalogram (EEG) in response to electrical stimulation with continuous pulse trains, and are distorted by significant CI artifacts related to this electrical stimulation. The aim of this study is to evaluate a CI artifacts attenuation method based on independent component analysis (ICA) for three EASSR datasets. Approach. ICA has often been used to remove CI artifacts from the EEG to record transient auditory responses, such as cortical evoked auditory potentials. Independent components (ICs) corresponding to CI artifacts are then often manually identified. In this study, an ICA based CI artifacts attenuation method was developed and evaluated for EASSR measurements with varying CI artifacts and EASSR characteristics. Artifactual ICs were automatically identified based on their spectrum. Main results. For 40 Hz amplitude modulation (AM) stimulation at comfort level, in high SNR recordings, ICA succeeded in removing CI artifacts from all recording channels, without distorting the EASSR. For lower SNR recordings, with 40 Hz AM stimulation at lower levels, or 90 Hz AM stimulation, ICA either distorted the EASSR or could not remove all CI artifacts in most subjects, except for two of the seven subjects tested with low level 40 Hz AM stimulation. Noise levels were reduced after ICA was applied, and up to 29 ICs were rejected, suggesting poor ICA separation quality. Significance. We hypothesize that ICA is capable of separating CI artifacts and EASSR in case the contralateral hemisphere is EASSR dominated. For small EASSRs or large CI artifact amplitudes, ICA separation quality is insufficient to ensure complete CI artifacts attenuation without EASSR distortion.

Performance comparison of six independent components analysis algorithms for fetal signal extraction from real fMCG data

International Nuclear Information System (INIS)

Hild, Kenneth E; Alleva, Giovanna; Nagarajan, Srikantan; Comani, Silvia

2007-01-01

In this study we compare the performance of six independent components analysis (ICA) algorithms on 16 real fetal magnetocardiographic (fMCG) datasets for the application of extracting the fetal cardiac signal. We also compare the extraction results for real data with the results previously obtained for synthetic data. The six ICA algorithms are FastICA, CubICA, JADE, Infomax, MRMI-SIG and TDSEP. The results obtained using real fMCG data indicate that the FastICA method consistently outperforms the others in regard to separation quality and that the performance of an ICA method that uses temporal information suffers in the presence of noise. These two results confirm the previous results obtained using synthetic fMCG data. There were also two notable differences between the studies based on real and synthetic data. The differences are that all six ICA algorithms are independent of gestational age and sensor dimensionality for synthetic data, but depend on gestational age and sensor dimensionality for real data. It is possible to explain these differences by assuming that the number of point sources needed to completely explain the data is larger than the dimensionality used in the ICA extraction

Comparison of conventional filtering and independent component analysis for artifact reduction in simultaneous gastric EMG and magnetogastrography from porcines.

Science.gov (United States)

Irimia, Andrei; Richards, William O; Bradshaw, L Alan

2009-11-01

In this study, we perform a comparative study of independent component analysis (ICA) and conventional filtering (CF) for the purpose of artifact reduction from simultaneous gastric EMG and magnetogastrography (MGG). EMG/MGG data were acquired from ten anesthetized pigs by obtaining simultaneous recordings using serosal electrodes (EMG) as well as with a superconducting quantum interference device biomagnetometer (MGG). The analysis of MGG waveforms using ICA and CF indicates that ICA is superior to the CF method in its ability to extract respiration and cardiac artifacts from MGG recordings. A signal frequency analysis of ICA- and CF-processed data was also undertaken using waterfall plots, and it was determined that the two methods produce qualitatively comparable results. Through the use of simultaneous EMG/MGG, we were able to demonstrate the accuracy and trustworthiness of our results by comparison and cross-validation within the framework of a porcine model.

A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2012-01-01

Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

Development of quantification methods for the myocardial blood flow using ensemble independent component analysis for dynamic H215O PET

International Nuclear Information System (INIS)

Lee, Byeong Il; Lee, Jae Sung; Lee, Dong Soo; Kang, Won Jun; Lee, Jong Jin; Kim, Soo Jin; Chung, June Key; Lee, Myung Chul; Choi, Seung Jin

2004-01-01

Factor analysis and independent component analysis (lCA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial H 2 15 O PET data. In this study, we quantified patients, blood flow using the ensemble ICA method. Twenty subjects underwent H 2 15 O PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of 555∼740 MBq H 2 15 O. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was 1.2±0.40 ml/min/g in rest, 1.85±1.12 ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis.

Science.gov (United States)

Hsieh, Sheng-Hsun; Li, Yung-Hui; Wang, Wei; Tien, Chung-Hao

2018-03-06

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme.

Independent component analysis separates spikes of different origin in the EEG.

Science.gov (United States)

Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

2006-02-01

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

Extracting intrinsic functional networks with feature-based group independent component analysis.

Science.gov (United States)

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro

An assessment of independent component analysis for detection of military targets from hyperspectral images

Science.gov (United States)

Tiwari, K. C.; Arora, M. K.; Singh, D.

2011-10-01

Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed-Xiaoli anomaly detector and Uniform Target Detector (RXD-UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.

Memory Efficient PCA Methods for Large Group ICA.

Science.gov (United States)

Rachakonda, Srinivas; Silva, Rogers F; Liu, Jingyu; Calhoun, Vince D

2016-01-01

Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT). The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads), accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4 GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations ideal for big

Memory efficient PCA methods for large group ICA

Directory of Open Access Journals (Sweden)

Srinivas eRachakonda

2016-02-01

Full Text Available Principal component analysis (PCA is widely used for data reduction in group independent component analysis (ICA of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT. The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads, accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations

Dual-energy x-ray image decomposition by independent component analysis

Science.gov (United States)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

Variational Bayesian Learning for Wavelet Independent Component Analysis

Science.gov (United States)

Roussos, E.; Roberts, S.; Daubechies, I.

2005-11-01

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely ill-posed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuro-scientific goal of extracting relevant "maps" from the data. This can be stated as a `blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid wavelet-ICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian mean-field approach in the conjugate-exponential family of distributions, for efficient unsupervised learning in multi-dimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments.

Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm

International Nuclear Information System (INIS)

Hong-Bin, Chen; Jiu-Chao, Feng; Yong, Fang

2008-01-01

We report the results of using the fast independent component analysis (FastICA) algorithm to realize blind extraction of chaotic signals. Two cases are taken into consideration: namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail. It is also shown that regarding a noise as an independent source is not always correct

Age-related differences in functional nodes of the brain cortex - a high model order group ICA study

Directory of Open Access Journals (Sweden)

Harri Littow

2010-08-01

Full Text Available Functional MRI measured with blood oxygen dependent (BOLD contrast in the absence of intermittent tasks reflects spontaneous activity of so called resting state networks (RSN of the brain. Group level independent component analysis (ICA of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age related effects from primary motor and sensory, and, higher level control RSNs. 168 healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 yrs, 59 young adults (YA, 22.2 ± 0.6yrs , and 54 older adults (OA, 42.7 ± 0.5 yrs, all with normal IQ. High model order group probabilistic ICA components (70 were calculated and dual regression analysis was used to compare 21 RSN’s spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN’s reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis

Science.gov (United States)

Hsieh, Sheng-Hsun; Wang, Wei; Tien, Chung-Hao

2018-01-01

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme. PMID:29509692

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis

Directory of Open Access Journals (Sweden)

Sheng-Hsun Hsieh

2018-03-01

Full Text Available In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme.

Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

Directory of Open Access Journals (Sweden)

Hagedorn Peter H

2011-02-01

Full Text Available Abstract Background Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA. Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes. Results Microrray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708 with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY. Conclusions In this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

Science.gov (United States)

Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

2015-02-20

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.

Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images

DEFF Research Database (Denmark)

Hansen, Michael Sass; Zhao, Fei; Zhang, Honghai

2006-01-01

Independent component analysis (ICA) is employed for com\\$\\backslash\\$-puter-aided diagnosis (CAD) allowing objective identification of subjects with connective tissue disorder from 4D aortic MR images. Stationary independent components assist in the disease detection, which is the first...

Signal Detection using ICA: Application to Chat Room Topic Spotting

DEFF Research Database (Denmark)

Kolenda, Thomas; Hansen, Lars Kai; Larsen, Jan

2001-01-01

signals with weak a priori assumptions in multimedia contexts. ICA of real world data is typically performed without knowledge of the number of non-trivial independent components, hence, it is of interest to test hypotheses concerning the number of components or simply to test whether a given set...... can detect meaningful context structures in a chat room log file....

Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis

DEFF Research Database (Denmark)

Hansen, Michael Sass; Zhao, Fei; Zhang, Honghai

2006-01-01

A computer-aided diagnosis (CAD) method is reported that allows the objective identification of subjects with connective tissue disorders from 3D aortic MR images using segmentation and independent component analysis (ICA). The first step to extend the model to 4D (3D + time) has also been taken....... ICA is an effective tool for connective tissue disease detection in the presence of sparse data using prior knowledge to order the components, and the components can be inspected visually. 3D+time MR image data sets acquired from 31 normal and connective tissue disorder subjects at end-diastole (R......-wave peak) and at 45\\$\\backslash\\$% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta. The CAD method distinguished between normal and connective tissue disorder subjects with a classification...

Blind Detection of Independent Dynamic Components

DEFF Research Database (Denmark)

Hansen, Lars Kai; Larsen, Jan; Kolenda, Thomas

2001-01-01

In certain applications of independent component analysis (ICA) it is of interest to test hypotheses concerning the number of components or simply to test whether a given number of components is significant relative to a "white noise" null hypothesis. We estimate probabilities of such competing h...

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

Science.gov (United States)

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Role of the two-component regulatory system arlRS in ica operon and aap positive but non-biofilm-forming Staphylococcus epidermidis isolates from hospitalized patients.

Science.gov (United States)

Wu, Yang; Liu, Jingran; Jiang, Juan; Hu, Jian; Xu, Tao; Wang, Jiaxue; Qu, Di

2014-11-01

The ica operon and aap gene are important factors for Staphylococcus epidermidis biofilm formation. However, we found 15 out of 101 S. epidermidis strains isolated from patients had both the ica operon and the aap gene in the genome but could not form biofilms (ica(+)aap(+)/BF(-) isolates). Compared with standard strain RP62A, the 15 ica(+)aap(+)/BF(-) isolates had similar growth curves and initial attachment abilities, but had much lower apparent transcription levels of the icaA gene and significantly less production of polysaccharide intercellular adhesion (PIA). Furthermore, the expression of accumulation-associated protein in ica(+)aap(+)/BF(-) isolates was much weaker than in RP62A. The mRNA levels of icaADBC transcription-related regulatory genes, including icaR, sarA, rsbU, srrA, arlRS and luxS, were measured in the 15 ica(+)aap(+)/BF(-) clinical isolates. The mRNA levels of arlR and rsbU in all of the ica(+)aap(+)/BF(-) isolates were lower than in RP62A at 4 h. At 10 h, 14/15 of the isolates showed lower mRNA levels of arlR and rsbU than shown by RP62A. However, expression of sarA, luxS, srrA and icaR varied in different ica(+)aap(+)/BF(-) isolates. To further investigate the role of arlRS in biofilm formation, we analyzed icaA, sarA and rsbU transcription, PIA synthesis, Aap expression and biofilm formation in an arlRS deletion mutant of S. epidermidis strain 1457 and all were much less than in the wild type strain. This is consistent with the hypothesis that ArlRS may play an important role in regulating biofilm formation by the ica(+)aap(+)/BF(-)S. epidermidis clinical isolates and operate via both ica-dependent and Aap-dependent pathways. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of Space Geodetic Time Series

Science.gov (United States)

Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano

2015-04-01

A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources

Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI

Science.gov (United States)

Mangalathu-Arumana, Jain; Liebenthal, Einat; Beardsley, Scott A.

2018-01-01

Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10–30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected. PMID:29410611

Multi-spectrometer calibration transfer based on independent component analysis.

Science.gov (United States)

Liu, Yan; Xu, Hao; Xia, Zhenzhen; Gong, Zhiyong

2018-02-26

Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.

A Novel Method for Surface Defect Detection of Photovoltaic Module Based on Independent Component Analysis

Directory of Open Access Journals (Sweden)

Xuewu Zhang

2013-01-01

Full Text Available This paper proposed a new method for surface defect detection of photovoltaic module based on independent component analysis (ICA reconstruction algorithm. Firstly, a faultless image is used as the training image. The demixing matrix and corresponding ICs are obtained by applying the ICA in the training image. Then we reorder the ICs according to the range values and reform the de-mixing matrix. Then the reformed de-mixing matrix is used to reconstruct the defect image. The resulting image can remove the background structures and enhance the local anomalies. Experimental results have shown that the proposed method can effectively detect the presence of defects in periodically patterned surfaces.

A comparative and combined study of EMIS and GPR detectors by the use of Independent Component Analysis

DEFF Research Database (Denmark)

Morgenstjerne, Axel; Karlsen, Brian; Larsen, Jan

2005-01-01

Independent Component Analysis (ICA) is applied to classify unexploded ordnance (UXO) on laboratory UXO test-field data, acquired by stand-off detection. The data are acquired by an Electromagnetic Induction Spectroscopy (EMIS) metal detector and a ground penetrating radar (GPR) detector. The metal...

Application of independent component analysis to H-1 MR spectroscopic imaging exams of brain tumours

NARCIS (Netherlands)

Szabo de Edelenyi, F.; Simonetti, A.W.; Postma, G.; Huo, R.; Buydens, L.M.C.

2005-01-01

The low spatial resolution of clinical H-1 MRSI leads to partial volume effects. To overcome this problem, we applied independent component analysis (ICA) on a set of H-1 MRSI exams of brain turnours. With this method, tissue types that yield statistically independent spectra can be separated. Up to

Evaluating Internal Communication: The ICA Communication Audit.

Science.gov (United States)

Goldhaber, Gerald M.

1978-01-01

The ICA Communication Audit is described in detail as an effective measurement procedure that can help an academic institution to evaluate its internal communication system. Tools, computer programs, analysis, and feedback procedures are described and illustrated. (JMF)

Independent component analysis using prior information for signal detection in a functional imaging system of the retina

NARCIS (Netherlands)

Barriga, E. Simon; Pattichis, Marios; Ts’o, Dan; Abramoff, Michael; Kardon, Randy; Kwon, Young; Soliz, Peter

2011-01-01

Independent component analysis (ICA) is a statistical technique that estimates a set of sources mixed by an unknown mixing matrix using only a set of observations. For this purpose, the only assumption is that the sources are statistically independent. In many applications, some information about

Independent component analysis: A new possibility for analysing series of electron energy loss spectra

International Nuclear Information System (INIS)

Bonnet, Nogl; Nuzillard, Danielle

2005-01-01

A complementary approach is proposed for analysing series of electron energy-loss spectra that can be recorded with the spectrum-line technique, across an interface for instance. This approach, called blind source separation (BSS) or independent component analysis (ICA), complements two existing methods: the spatial difference approach and multivariate statistical analysis. The principle of the technique is presented and illustrations are given through one simulated example and one real example

Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Science.gov (United States)

Ai, Dan-Ni; Han, Xian-Hua; Ruan, Xiang; Chen, Yen-Wei

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

Fingerprint separation: an application of ICA

Science.gov (United States)

Singh, Meenakshi; Singh, Deepak Kumar; Kalra, Prem Kumar

2008-04-01

Among all existing biometric techniques, fingerprint-based identification is the oldest method, which has been successfully used in numerous applications. Fingerprint-based identification is the most recognized tool in biometrics because of its reliability and accuracy. Fingerprint identification is done by matching questioned and known friction skin ridge impressions from fingers, palms, and toes to determine if the impressions are from the same finger (or palm, toe, etc.). There are many fingerprint matching algorithms which automate and facilitate the job of fingerprint matching, but for any of these algorithms matching can be difficult if the fingerprints are overlapped or mixed. In this paper, we have proposed a new algorithm for separating overlapped or mixed fingerprints so that the performance of the matching algorithms will improve when they are fed with these inputs. Independent Component Analysis (ICA) has been used as a tool to separate the overlapped or mixed fingerprints.

Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra.

Science.gov (United States)

Mantini, Dante; Petrucci, Francesca; Del Boccio, Piero; Pieragostino, Damiana; Di Nicola, Marta; Lugaresi, Alessandra; Federici, Giorgio; Sacchetta, Paolo; Di Ilio, Carmine; Urbani, Andrea

2008-01-01

Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra. The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.

On applicability of PCA, voxel-wise variance normalization and dimensionality assumptions for sliding temporal window sICA in resting-state fMRI.

Science.gov (United States)

Remes, Jukka J; Abou Elseoud, Ahmed; Ollila, Esa; Haapea, Marianne; Starck, Tuomo; Nikkinen, Juha; Tervonen, Osmo; Silven, Olli

2013-10-01

Subject-level resting-state fMRI (RS-fMRI) spatial independent component analysis (sICA) may provide new ways to analyze the data when performed in the sliding time window. However, whether principal component analysis (PCA) and voxel-wise variance normalization (VN) are applicable pre-processing procedures in the sliding-window context, as they are for regular sICA, has not been addressed so far. Also model order selection requires further studies concerning sliding-window sICA. In this paper we have addressed these concerns. First, we compared PCA-retained subspaces concerning overlapping parts of consecutive temporal windows to answer whether in-window PCA and VN can confound comparisons between sICA analyses in consecutive windows. Second, we compared the PCA subspaces between windowed and full data to assess expected comparability between windowed and full-data sICA results. Third, temporal evolution of dimensionality estimates in RS-fMRI data sets was monitored to identify potential challenges in model order selection in a sliding-window sICA context. Our results illustrate that in-window VN can be safely used, in-window PCA is applicable with most window widths and that comparisons between windowed and full data should not be performed from a subspace similarity point of view. In addition, our studies on dimensionality estimates demonstrated that there are sustained, periodic and very case-specific changes in signal-to-noise ratio within RS-fMRI data sets. Consequently, dimensionality estimation is needed for well-founded model order determination in the sliding-window case. The observed periodic changes correspond to a frequency band of ≤0.1 Hz, which is commonly associated with brain activity in RS-fMRI and become on average most pronounced at window widths of 80 and 60 time points (144 and 108 s, respectively). Wider windows provided only slightly better comparability between consecutive windows, and 60 time point or shorter windows also provided the

Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking.

Directory of Open Access Journals (Sweden)

Kristine Lynne Snyder

2015-12-01

Full Text Available There has been a recent surge in the use of electroencephalography (EEG as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

Science.gov (United States)

Forootan, Ehsan; Kusche, Jürgen

2016-04-01

Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

Science.gov (United States)

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia

Directory of Open Access Journals (Sweden)

Cota Navin Gupta

2017-09-01

Full Text Available Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA. This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS positive clinical symptoms (p = 0.005. We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07. The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the

Geomorphology Characterization of Ica Basin and Its Influence on the Dynamic Response of Soils for Urban Seismic Hazards in Ica, Peru

Directory of Open Access Journals (Sweden)

Isabel Bernal

2018-01-01

Full Text Available We evaluated the influence of the geomorphology of Peru’s Ica Basin on the dynamic response of soils of the city of Ica. We applied five geophysical methods: spectral ratio (H/V, frequency-wavenumber (F-K, multichannel analysis of surface waves (MASW, multichannel analysis of microtremor (MAM, and Gravimetric Analysis. Our results indicate that the soils respond to two frequency ranges: F0 (0.4–0.8 Hz and F1 (1.0–3.0 Hz. The F-K, which considers circular arrays, shows two tendencies with a jump between 1.0 and 2.0 Hz. MASW and MAM contribute to frequencies greater than 2.0 Hz. The inversion curve indicates the presence of three layers of 4, 16, and 60 m with velocities of 180, 250, and 400 m/s. The Bouguer anomalies vary between −17.72 and −24.32 mGal and with the spectral analysis we identified two deposits, of 60 m and 150 m of thickness. Likewise, the relationship between the velocities of 400 and 900 m/s, with the frequency = 1.5 Hz, allows us to determine the thickness for the layers of 60 (slightly alluvial to moderately compact and 150 m (soil-rock interface. These results suggest that the morphology of the Ica Basin plays an important role in the dynamic behavior of the soils to low frequency.

Cerebral blood flow and CO2 reactivity in transient ischemic attacks: comparison between TIAs due to the ICA occlusion and ICA mild stenosis

International Nuclear Information System (INIS)

Tsuda, Y.; Kimura, K.; Yoneda, S.; Etani, H.; Asai, T.; Nakamura, M.; Abe, H.

1983-01-01

Hemispheric mean cerebral blood flow (CBF), together with its CO2 reactivity in response to hyperventilation, was investigated in 18 patients with transient ischemic attacks (TIAs) by intraarterial 133Xe injection method in a subacute-chronic stage of the clinical course. In 8 patients, the lesion responsible for symptoms was regarded as unilateral internal carotid artery (ICA) occlusion, and in 10 patients, it was regarded as unilateral ICA mild stenosis (less than 50% stenosis in diameter). Resting flow values were significantly decreased in the affected hemisphere of TIA due to the ICA occlusion as compared with the unaffected hemisphere of the same patient, regarded as the relative control. It was not decreased in the affected hemisphere of TIA due to the ICA mild stenosis as compared with the control. With respect to the responsiveness of CBF to changes in PaCO2, it was preserved in both TIAs, due to the ICA occlusion and ICA mild stenosis. Vasoparalysis was not observed in either types of TIAs in the subacute-chronic stage. However, in the relationship of blood pressure and CO2 reactivity, expressed as delta CBF(%)/delta PaCO2, pressure-dependent CO2 reactivity as a group was observed with significance in 8 cases of TIA due to the ICA occlusion, while no such relationship was noted in 10 cases of TIA due to the ICA mild stenosis. Moreover, clinical features were different between TIAs due to the ICA occlusion and ICA mild stenosis, i.e., more typical, repeatable TIA (6.3 +/- 3.7 times) with shorter duration (less than 30 minutes) was observed in TIAs due to the ICA mild stenosis, while more prolonged, less repeatable TIA (2.4 +/- 1.4 times) was observed in TIAs due to fixed obstruction of the ICA. From these observations, two different possible mechanisms as to the pathogenesis of TIA might be expected

Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

Science.gov (United States)

Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

2018-02-01

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

Directory of Open Access Journals (Sweden)

Dong-Sup Lee

2015-01-01

Full Text Available Independent Component Analysis (ICA, one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: insta- bility and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to vali- date the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

Science.gov (United States)

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Independent principal component analysis for simulation of soil water content and bulk density in a Canadian Watershed

Directory of Open Access Journals (Sweden)

Alaba Boluwade

2016-09-01

Full Text Available Accurate characterization of soil properties such as soil water content (SWC and bulk density (BD is vital for hydrologic processes and thus, it is importance to estimate θ (water content and ρ (soil bulk density among other soil surface parameters involved in water retention and infiltration, runoff generation and water erosion, etc. The spatial estimation of these soil properties are important in guiding agricultural management decisions. These soil properties vary both in space and time and are correlated. Therefore, it is important to find an efficient and robust technique to simulate spatially correlated variables. Methods such as principal component analysis (PCA and independent component analysis (ICA can be used for the joint simulations of spatially correlated variables, but they are not without their flaws. This study applied a variant of PCA called independent principal component analysis (IPCA that combines the strengths of both PCA and ICA for spatial simulation of SWC and BD using the soil data set from an 11 km2 Castor watershed in southern Quebec, Canada. Diagnostic checks using the histograms and cumulative distribution function (cdf both raw and back transformed simulations show good agreement. Therefore, the results from this study has potential in characterization of water content variability and bulk density variation for precision agriculture.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

Science.gov (United States)

Ye, Zheng; Doñamayor, Nuria; Münte, Thomas F

2014-02-01

A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging (fMRI) study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis (ICA) and graph theoretical analysis (GTA). The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area (SMA), left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs. congruent endings. Copyright © 2012 Wiley Periodicals, Inc.

Stealthy false data injection attacks using matrix recovery and independent component analysis in smart grid

Science.gov (United States)

JiWei, Tian; BuHong, Wang; FuTe, Shang; Shuaiqi, Liu

2017-05-01

Exact state estimation is vital important to maintain common operations of smart grids. Existing researches demonstrate that state estimation output could be compromised by malicious attacks. However, to construct the attack vectors, a usual presumption in most works is that the attacker has perfect information regarding the topology and so on even such information is difficult to acquire in practice. Recent research shows that Independent Component Analysis (ICA) can be used for inferring topology information which can be used to originate undetectable attacks and even to alter the price of electricity for the profits of attackers. However, we found that the above ICA-based blind attack tactics is merely feasible in the environment with Gaussian noises. If there are outliers (device malfunction and communication errors), the Bad Data Detector will easily detect the attack. Hence, we propose a robust ICA based blind attack strategy that one can use matrix recovery to circumvent the outlier problem and construct stealthy attack vectors. The proposed attack strategies are tested with IEEE representative 14-bus system. Simulations verify the feasibility of the proposed method.

Improved separability of dipole sources by tripolar versus conventional disk electrodes: a modeling study using independent component analysis.

Science.gov (United States)

Cao, H; Besio, W; Jones, S; Medvedev, A

2009-01-01

Tripolar electrodes have been shown to have less mutual information and higher spatial resolution than disc electrodes. In this work, a four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes, and four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements, and talking. Finally, a fast fixed-point algorithm was used for signal independent component analysis (ICA). The results show that signals from tripolar electrodes generated better ICA separation results than from disc electrodes for EEG signals with these four types of artifacts.

Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location

Science.gov (United States)

Liu, Jiangang; Tian, Jie

2007-03-01

The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging

The ICA Communication Audit: Rationale and Development.

Science.gov (United States)

Goldhaber, Gerald M.

After reviewing previous research on communication in organizations, the Organizational Communication Division of the International Communication Association (ICA) decided, in 1971, to develop its own measurement system, the ICA Communication Audit. Rigorous pilot-testing, refinement, standardization, and application would allow the construction…

Performance limits of ICA-based heart rate identification techniques in imaging photoplethysmography

International Nuclear Information System (INIS)

Mannapperuma, Kavan; Holton, Benjamin D; Lesniewski, Peter J; Thomas, John C

2015-01-01

Imaging photoplethysmography is a relatively new technique for extracting biometric information from video images of faces. This is useful in non-invasive monitoring of patients including neonates or the aged, with respect to sudden infant death syndrome, sleep apnoea, pulmonary disease, physical or mental stress and other cardio-vascular conditions. In this paper, we investigate the limits of detection of the heart rate (HR) while reducing the video quality. We compare the performance of three independent component analysis (ICA) methods (JADE, FastICA, RADICAL), autocorrelation with signal conditioning techniques and identify the most robust approach. We discuss sources of increasing error and other limiting conditions in three situations of reduced signal-to-noise ratio: one where the area of the analyzed face is decreased from 100 to 5%, another where the face area is progressively re-sampled down to a single RGB pixel and one where the HR signal is severely reduced with respect to the boundary noise. In most cases, the cardiac pulse rate can be reliably and accurately detected from videos containing only 5% facial area or from a face occupying just 4 pixels or containing only 5% of the facial HR modulation. (paper)

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

Science.gov (United States)

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

Directory of Open Access Journals (Sweden)

Juha M Lahnakoski

Full Text Available Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA. Auditory annotations correlated with two independent components (IC disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

All-phase MR angiography using independent component analysis of dynamic contrast enhanced MRI time series. φ-MRA

International Nuclear Information System (INIS)

Suzuki, Kiyotaka; Matsuzawa, Hitoshi; Watanabe, Masaki; Nakada, Tsutomu; Nakayama, Naoki; Kwee, I.L.

2003-01-01

Dynamic contrast enhanced magnetic resonance imaging (dynamic MRI) represents a MRI version of non-diffusible tracer methods, the main clinical use of which is the physiological construction of what is conventionally referred to as perfusion images. The raw data utilized for constructing MRI perfusion images are time series of pixel signal alterations associated with the passage of a gadolinium containing contrast agent. Such time series are highly compatible with independent component analysis (ICA), a novel statistical signal processing technique capable of effectively separating a single mixture of multiple signals into their original independent source signals (blind separation). Accordingly, we applied ICA to dynamic MRI time series. The technique was found to be powerful, allowing for hitherto unobtainable assessment of regional cerebral hemodynamics in vivo. (author)

Principal Component Analysis In Radar Polarimetry

Directory of Open Access Journals (Sweden)

A. Danklmayer

2005-01-01

Full Text Available Second order moments of multivariate (often Gaussian joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix. In Radar Polarimetry the application of the covariance matrix is known as target decomposition theory, which is a special application of the extremely versatile Principle Component Analysis (PCA. The basic idea of PCA is to convert a data set, consisting of correlated random variables into a new set of uncorrelated variables and order the new variables according to the value of their variances. It is important to stress that uncorrelatedness does not necessarily mean independent which is used in the much stronger concept of Independent Component Analysis (ICA. Both concepts agree for multivariate Gaussian distribution functions, representing the most random and least structured distribution. In this contribution, we propose a new approach in applying the concept of PCA to Radar Polarimetry. Therefore, new uncorrelated random variables will be introduced by means of linear transformations with well determined loading coefficients. This in turn, will allow the decomposition of the original random backscattering target variables into three point targets with new random uncorrelated variables whose variances agree with the eigenvalues of the covariance matrix. This allows a new interpretation of existing decomposition theorems.

Dealing with ocular artifacts on lateralized ERPs in studies of visual-spatial attention and memory: ICA correction versus epoch rejection.

Science.gov (United States)

Drisdelle, Brandi Lee; Aubin, Sébrina; Jolicoeur, Pierre

2017-01-01

The objective of the present study was to assess the robustness and reliability of independent component analysis (ICA) as a method for ocular artifact correction in electrophysiological studies of visual-spatial attention and memory. The N2pc and sustained posterior contralateral negativity (SPCN), electrophysiological markers of visual-spatial attention and memory, respectively, are lateralized posterior ERPs typically observed following the presentation of lateral stimuli (targets and distractors) along with instructions to maintain fixation on the center of the visual search for the entire trial. Traditionally, trials in which subjects may have displaced their gaze are rejected based on a cutoff threshold, minimizing electrophysiological contamination by saccades. Given the loss of data resulting from rejection, we examined ocular correction by comparing results using standard fixation instructions against a condition where subjects were instructed to shift their gaze toward possible targets. Both conditions were analyzed using a rejection threshold and ICA correction for saccade activity management. Results demonstrate that ICA conserves data that would have otherwise been removed and leaves the underlying neural activity intact, as demonstrated by experimental manipulations previously shown to modulate the N2pc and the SPCN. Not only does ICA salvage and not distort data, but also large eye movements had only subtle effects. Overall, the findings provide convincing evidence for ICA correction for not only special cases (e.g., subjects did not follow fixation instruction) but also as a candidate for standard ocular artifact management in electrophysiological studies interested in visual-spatial attention and memory. © 2016 Society for Psychophysiological Research.

Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: The case of an ATM explosion.

Science.gov (United States)

Almeida, Mariana Ramos; Logrado, Lucio Paulo Lima; Zacca, Jorge Jardim; Correa, Deleon Nascimento; Poppi, Ronei Jesus

2017-11-01

In this work, Raman hyperspectral imaging, in conjunction with independent component analysis, was employed as an analytical methodology to detect an ammonium nitrate fuel oil (ANFO) explosive in banknotes after an ATM explosion experiment. The proposed methodology allows for the identification of the ANFO explosive without sample preparation or destroying the sample, at quantities as small as 70μgcm -2 . The explosive was identified following ICA data decomposition by the characteristic nitrate band at 1044cm -1 . The use of Raman hyperspectral imaging and independent component analysis shows great potential for identifying forensic samples by providing chemical and spatial information. Copyright © 2017 Elsevier B.V. All rights reserved.

REPEATABILITY OF SPITZER/IRAC EXOPLANETARY ECLIPSES WITH INDEPENDENT COMPONENT ANALYSIS

Energy Technology Data Exchange (ETDEWEB)

Morello, G.; Waldmann, I. P.; Tinetti, G., E-mail: giuseppe.morello.11@ucl.ac.uk [Department of Physics and Astronomy, University College London, Gower Street, WC1E6BT (United Kingdom)

2016-04-01

The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid- and far-infrared, at wavelengths >3 μm, have been taken with Spitzer. In some cases, in past years, repeated observations and multiple reanalyses of the same data sets led to discrepant results, raising questions about the accuracy and reproducibility of such measurements. Morello et al. (2014, 2015) proposed a blind-source separation method based on the Independent Component Analysis of pixel time series (pixel-ICA) to analyze InfraRed Array Camera (IRAC) data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to the pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-signal-to-noise-ratio cases. We describe the method and discuss the results obtained over 12 eclipses of the exoplanet XO3b observed during the “Warm Spitzer” era in the 4.5 μm band. The final results are reported, in part, also in Ingalls et al. (2016), together with results obtained with other detrending methods, and over 10 synthetic eclipses that were analyzed for the “IRAC Data Challenge 2015.” Our results are consistent within 1σ with the ones reported in Wong et al. (2014) and with most of the results reported in Ingalls et al. (2016), which appeared on arXiv while this paper was under review. Based on many statistical tests discussed in Ingalls et al. (2016), the wavelet pixel-ICA method performs as well as or better than other state-of-art methods recently developed by other teams to analyze Spitzer/IRAC data, and, in particular, it appears to be the most repeatable and the most reliable, while reaching the photon noise limit, at least for the particular data set analyzed. Another strength of the ICA approach is its highest

REPEATABILITY OF SPITZER/IRAC EXOPLANETARY ECLIPSES WITH INDEPENDENT COMPONENT ANALYSIS

International Nuclear Information System (INIS)

Morello, G.; Waldmann, I. P.; Tinetti, G.

2016-01-01

The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid- and far-infrared, at wavelengths >3 μm, have been taken with Spitzer. In some cases, in past years, repeated observations and multiple reanalyses of the same data sets led to discrepant results, raising questions about the accuracy and reproducibility of such measurements. Morello et al. (2014, 2015) proposed a blind-source separation method based on the Independent Component Analysis of pixel time series (pixel-ICA) to analyze InfraRed Array Camera (IRAC) data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to the pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-signal-to-noise-ratio cases. We describe the method and discuss the results obtained over 12 eclipses of the exoplanet XO3b observed during the “Warm Spitzer” era in the 4.5 μm band. The final results are reported, in part, also in Ingalls et al. (2016), together with results obtained with other detrending methods, and over 10 synthetic eclipses that were analyzed for the “IRAC Data Challenge 2015.” Our results are consistent within 1σ with the ones reported in Wong et al. (2014) and with most of the results reported in Ingalls et al. (2016), which appeared on arXiv while this paper was under review. Based on many statistical tests discussed in Ingalls et al. (2016), the wavelet pixel-ICA method performs as well as or better than other state-of-art methods recently developed by other teams to analyze Spitzer/IRAC data, and, in particular, it appears to be the most repeatable and the most reliable, while reaching the photon noise limit, at least for the particular data set analyzed. Another strength of the ICA approach is its highest

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

Science.gov (United States)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data

Directory of Open Access Journals (Sweden)

Shanshan eLi

2016-01-01

Full Text Available Independent Component analysis (ICA is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.

Independent component analysis of edge information for face recognition

CERN Document Server

Karande, Kailash Jagannath

2013-01-01

The book presents research work on face recognition using edge information as features for face recognition with ICA algorithms. The independent components are extracted from edge information. These independent components are used with classifiers to match the facial images for recognition purpose. In their study, authors have explored Canny and LOG edge detectors as standard edge detection methods. Oriented Laplacian of Gaussian (OLOG) method is explored to extract the edge information with different orientations of Laplacian pyramid. Multiscale wavelet model for edge detection is also propos

Time course based artifact identification for independent components of resting state fMRI

Directory of Open Access Journals (Sweden)

Christian eRummel

2013-05-01

Full Text Available In functional magnetic resonance imaging (fMRI coherent oscillations of the blood oxygen level dependent (BOLD signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting state networks (RSN. Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82 and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

Science.gov (United States)

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Brain Functional Connectivity in Small Cell Lung Cancer Population after Chemotherapy Treatment: an ICA fMRI Study

Science.gov (United States)

Bromis, K.; Kakkos, I.; Gkiatis, K.; Karanasiou, I. S.; Matsopoulos, G. K.

2017-11-01

Previous neurocognitive assessments in Small Cell Lung Cancer (SCLC) population, highlight the presence of neurocognitive impairments (mainly in attention processing and executive functioning) in this type of cancer. The majority of these studies, associate these deficits with the Prophylactic Cranial Irradiation (PCI) that patients undergo in order to avoid brain metastasis. However, there is not much evidence exploring cognitive impairments induced by chemotherapy in SCLC patients. For this reason, we aimed to investigate the underlying processes that may potentially affect cognition by examining brain functional connectivity in nineteen SCLC patients after chemotherapy treatment, while additionally including fourteen healthy participants as control group. Independent Component Analysis (ICA) is a functional connectivity measure aiming to unravel the temporal correlation between brain regions, which are called brain networks. We focused on two brain networks related to the aforementioned cognitive functions, the Default Mode Network (DMN) and the Task-Positive Network (TPN). Permutation tests were performed between the two groups to assess the differences and control for familywise errors in the statistical parametric maps. ICA analysis showed functional connectivity disruptions within both of the investigated networks. These results, propose a detrimental effect of chemotherapy on brain functioning in the SCLC population.

Abnormal Functional Connectivity of Resting State Network Detection Based on Linear ICA Analysis in Autism Spectrum Disorder.

Science.gov (United States)

Bi, Xia-An; Zhao, Junxia; Xu, Qian; Sun, Qi; Wang, Zhigang

2018-01-01

Some functional magnetic resonance imaging (fMRI) researches in autism spectrum disorder (ASD) patients have shown that ASD patients have significant impairment in brain response. However, few researchers have studied the functional structure changes of the eight resting state networks (RSNs) in ASD patients. Therefore, research on statistical differences of RSNs between 42 healthy controls (HC) and 50 ASD patients has been studied using linear independent component analysis (ICA) in this paper. Our researches showed that there was abnormal functional connectivity (FC) of RSNs in ASD patients. The RSNs with the decreased FC and increased FC in ASD patients included default mode network (DMN), central executive network (CEN), core network (CN), visual network (VN), self-referential network (SRN) compared to HC. The RSNs with the increased FC in ASD patients included auditory network (AN), somato-motor network (SMN). The dorsal attention network (DAN) in ASD patients showed the decreased FC. Our findings indicate that the abnormal FC in RSNs extensively exists in ASD patients. Our results have important contribution for the study of neuro-pathophysiological mechanisms in ASD patients.

Comparison of Biofilm Formation Capacities of Two Clinical Isolates of Staphylococcus Epidermidis with and without icaA and icaD Genes on Intraocular Lenses

Directory of Open Access Journals (Sweden)

Sertaç Argun Kıvanç

2017-03-01

Full Text Available Objectives: To compare biofilm formations of two Staphylococcus epidermidis (S. epidermidis isolates with known biofilm formation capacities on four different intraocular lenses (IOL that have not been studied before. Materials and Methods: Two isolates obtained from ocular surfaces and identified in previous studies and stored at -86 °C in 15% glycerol in the microbiology laboratory of the Anadolu University Department of Biology were purified and used in the study. The isolates were S. epidermidis KA 15.8 (ICA+, a known biofilm producer isolate positive for icaA, icaD and bap genes, and S. epidermidis KA 14.5 (ICA-, known as a non-biofilm producer isolate negative for icaA, icaD and bap genes. The biofilm formation capacities of the 2 isolates on 4 different IOLs were compared. Two of the IOLs were acrylic (UD613 [IOL A], Turkey; SA60AT [IOL B], USA, and the other two were polymethyl methacrylate (PMMA (B60130C [IOL C], India; B55125C [IOL D], India. Bacterial enumeration and optical density measurements were done from biofilms that formed on the IOLs. Biofilms were imaged using scanning electron microscopy. Results: Mean bacterial counts on the IOLs were 7.1±0.4 log10 CFU/mL with the ICA+ isolate, and 6.7±0.8 log10 CFU/mL with the ICA- isolate; there were no statistically significant differences. Biofilm formation was lower with acrylic lenses than PMMA lenses with both isolates (p=0.009 and p=0.013. The highest biofilm production was obtained on IOL C (PMMA (p<0.001 and the lowest was obtained on IOL A (hydrophilic acrylic (p<0.001. Conclusion: Bacterial counts after biofilm formation were lower on acrylic lenses, especially hydrophilic acrylic with hydrophobic properties. Further animal and in vivo studies are required to support the findings of this study.

Tempo-spatial analysis of vision-related acupoint specificity in the occipital lobe using fMRI: an ICA study.

Science.gov (United States)

Dong, Minghao; Qin, Wei; Sun, Jinbo; Liu, Peng; Yuan, Kai; Liu, Jixin; Zhou, Guangyu; von Deneen, Karen M; Tian, Jie

2012-02-03

Functional acupoint specificity is one of the most debated topics in acupuncture neuroimaging research. Conventional studies investigating vision-related acupoint specificity empirically assume that acupuncture-induced hemodynamic response can be defined a priori and thus concentrate on distinguishing the spatial variations of response patterns across acupoints in the occipital lobe. However, evidence suggests that acupuncture-invoked BOLD signal changes are independent of a priori time shape. Additionally, temporal profiles reflect how a stimulus corresponds with the brain, implying the hemodynamic coherence induced by stimulation. Therefore, temporal information carried in acupuncture-related neural activity may be more crucial to specificity issues. This paper initiates the detection into tempo-spatial dimension and the goal of this study is to detect functional acupoint specificity by uniquely comparing the temporal activities of the occipital lobe among vision-related acupoints (VRA) and a non-acupoint (NAP). We utilized the independent component analysis (ICA) to extract temporal patterns of occipital response by stimulating a VRA, i.e. GB37, and a NAP. As an improvement over previous ones, another VRA, i.e., BL60 was employed to consolidate our findings. Results showed that although all groups showed V1 activity in the occipital lobe, dissociable temporal activities in this region categorized GB37 and NAP (r=0.05, p=0.64). This finding was replicable with regard to BL60 and NAP (r=-0.03, p=0.77). Intriguingly, stimulation at two VRAs induced highly correlated temporal activities (p<0.0001). This study adds positive evidence to the issue of vision-related acupoint specificity. The utilization of ICA and consideration of temporal dynamics may shed light on future studies. Copyright © 2011 Elsevier B.V. All rights reserved.

Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

Directory of Open Access Journals (Sweden)

Robert Laforce, Jr

2014-01-01

Full Text Available The relationships between clinical phenotype, β-amyloid (Aβ deposition and neurodegeneration in Alzheimer's disease (AD are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET data from a clinically heterogeneous population of patients with probable AD in order to: (1 identify spatial patterns of Aβ deposition measured by (11C-labeled Pittsburgh Compound B (PiB-PET and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2 explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA–AA who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8. Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27, a language-predominant group (AD-language; n = 10 and a visuospatial-predominant group (AD-visuospatial; n = 9. All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA, a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011, and two components

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Science.gov (United States)

Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

2012-12-01

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that

Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

Science.gov (United States)

Nugent, Allison C; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

2017-02-01

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Selection of independent components based on cortical mapping of electromagnetic activity

Science.gov (United States)

Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

2012-10-01

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

Induced radio-mutagenesis of some food enzyme forming microorganisms of the I.C.A. collection

International Nuclear Information System (INIS)

Dumitru, E.; Ferdes, M.; Catargiu, L.; Ferdes, O.S.; Mencinicopschi, G.

1994-01-01

In the paper there is described the research carried out regarding the use of gamma radiations as mutagen agent in bio technologies and the explanation of the action mechanisms of these radiations at level of genetic material. Experiments were made on the following strains of I.C.A. collection: B. subtilis I.C.A. collection 1.65; Aspergillus niger I.C.A. 3.49 and I.C.A. 3.76, and Trichoderma viride I.C.A. 3.196. The irradiations were made by gamma radiations at the 60 Co isotope source of I.C.C.F. having an activity of about 370 TBq in a 1.0 - 10.0 KGy dose range at different dose rates under normal conditions. There were isolated 5 mutants strains of B. subtilis I.C.A. 1.65, 2 strains of Aspergillus niger I.C.A. 3.76, 2 strains of Aspergillus niger I.C.A. 3.49, and 2 possible mutants of Trichoderma viride I.C.A. 3.196. (Author)

GPR Detection of Buried Symmetrically Shaped Mine-like Objects using Selective Independent Component Analysis

DEFF Research Database (Denmark)

Karlsen, Brian; Sørensen, Helge Bjarup Dissing; Larsen, Jan

2003-01-01

from small-scale anti-personal (AP) mines to large-scale anti-tank (AT) mines were designed. Large-scale SF-GPR measurements on this series of mine-like objects buried in soil were performed. The SF-GPR data was acquired using a wideband monostatic bow-tie antenna operating in the frequency range 750......This paper addresses the detection of mine-like objects in stepped-frequency ground penetrating radar (SF-GPR) data as a function of object size, object content, and burial depth. The detection approach is based on a Selective Independent Component Analysis (SICA). SICA provides an automatic...... ranking of components, which enables the suppression of clutter, hence extraction of components carrying mine information. The goal of the investigation is to evaluate various time and frequency domain ICA approaches based on SICA. Performance comparison is based on a series of mine-like objects ranging...

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study.

Science.gov (United States)

Yu, Qingbao; Du, Yuhui; Chen, Jiayu; He, Hao; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D

2017-11-01

A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

Science.gov (United States)

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

Amerikanisierung durch Internationalisierung: Die Expansion der International Communication Association (ICA

Directory of Open Access Journals (Sweden)

Thomas Wiedemann

2016-12-01

Full Text Available Basierend auf der Soziologie Bourdieus problematisiert dieser Beitrag die Bemühungen der International Communication Association (ICA, sich über die Öffnung ihrer Führungsetage für Wissenschaftler außerhalb der USA in eine wahrhaft internationale Fachgesellschaft zu verwandeln und der Herausforderung einer global vernetzten Disziplin zu begegnen. Geleistet werden soll so ein kritischer Beitrag zur Selbstreflexion der Kommunikationswissenschaft, zu verstehen als Deutungsangebot und Ausgangspunkt für die wissenschaftliche Diskussion. Die Untersuchung von Habitus und Kapital der 29 ICA-Präsidenten und ICA Fellows aus der internationalen Scientific Community zeigt, dass die weltweit größte kommunikationswissenschaftliche Fachgesellschaft trotz der Ausweitung ihrer Führungsriege immer noch deutlichen US-Einflüssen unterliegt. Die neuen ICAWürdenträger, die für nationale und fachliche Vielfalt stehen sollen, stammen aus Weltregionen, die eine besondere Nähe zu den Vereinigten Staaten auszeichnet, und wurden an US-Universitäten sozialisiert oder stark von der US-amerikanischen Forschungstradition geprägt. Ausnahmen („Einkäufe“ von führenden Vertretern anderer Fachgesellschaften oder alternativer Paradigmen bestätigen die Regel. Die Internationalisierung der ICA-Führungsetage veränderte demzufolge weniger den Machtpol im Fach als vielmehr die Kommunikationswissenschaft weltweit. Zwar gelangten neue Perspektiven ins Zentrum der Disziplin. Im Gegenzug fand jedoch eine Amerikanisierung nationaler Felder statt, allen voran durch ICA Fellows als Vorbilder im Kampf um wissenschaftliches Kapital. Die Bemühungen der ICA, sich durch die Expansion ihrer Führungsriege zu internationalisieren, dürften somit die Machtstrukturen im Fach weiter verfestigt haben.

Gas Bubbles Investigation in Contaminated Water Using Optical Tomography Based on Independent Component Analysis Method

Directory of Open Access Journals (Sweden)

Mohd Taufiq Mohd Khairi

2016-01-01

Full Text Available This paper presents the results of concentration profiles for gas bubble flow in a vertical pipeline containing contaminated water using an optical tomography system. The concentration profiles for the bubble flow quantities are investigated under five different flows conditions, a single bubble, double bubbles, 25% of air opening, 50% of air opening, and 100% of air opening flow rates where a valve is used to control the gas flow in the vertical pipeline. The system is aided by the independent component analysis (ICA algorithm to reconstruct the concentration profiles of the liquid-gas flow. The behaviour of the gas bubbles was investigated in contaminated water in which the water sample was prepared by adding 25 mL of colour ingredients to 3 liters of pure water. The result shows that the application of ICA has enabled the system to detect the presence of gas bubbles in contaminated water. This information provides vital information on the flow inside the pipe and hence could be very significant in increasing the efficiency of the process industries.

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.

Directory of Open Access Journals (Sweden)

Rogier Alexander Feis

2015-10-01

Full Text Available Resting-state fMRI (R-fMRI has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects.We analyzed 3 Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA, FMRIB’s ICA-based X-noiseifier (FIX was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX.Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically different.FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

Science.gov (United States)

Schoenwald, Adam; Mohammed, Priscilla; Bradley, Damon; Piepmeier, Jeffrey; Wong, Englin; Gholian, Armen

2016-01-01

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor

Science.gov (United States)

Schoenwald, Adam J.; Gholian, Armen; Bradley, Damon C.; Wong, Mark; Mohammed, Priscilla N.; Piepmeier, Jeffrey R.

2016-01-01

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

[Changing the internal cost allocation (ICA) on DRG shares : Example of computed tomography in a university radiology setting].

Science.gov (United States)

Wirth, K; Zielinski, P; Trinter, T; Stahl, R; Mück, F; Reiser, M; Wirth, S

2016-08-01

In hospitals, the radiological services provided to non-privately insured in-house patients are mostly distributed to requesting disciplines through internal cost allocation (ICA). In many institutions, computed tomography (CT) is the modality with the largest amount of allocation credits. The aim of this work is to compare the ICA to respective DRG (Diagnosis Related Groups) shares for diagnostic CT services in a university hospital setting. The data from four CT scanners in a large university hospital were processed for the 2012 fiscal year. For each of the 50 DRG groups with the most case-mix points, all diagnostic CT services were documented including their respective amount of GOÄ allocation credits and invoiced ICA value. As the German Institute for Reimbursement of Hospitals (InEK) database groups the radiation disciplines (radiology, nuclear medicine and radiation therapy) together and also lacks any modality differentiation, the determination of the diagnostic CT component was based on the existing institutional distribution of ICA allocations. Within the included 24,854 cases, 63,062,060 GOÄ-based performance credits were counted. The ICA relieved these diagnostic CT services by € 819,029 (single credit value of 1.30 Eurocent), whereas accounting by using DRG shares would have resulted in € 1,127,591 (single credit value of 1.79 Eurocent). The GOÄ single credit value is 5.62 Eurocent. The diagnostic CT service was basically rendered as relatively inexpensive. In addition to a better financial result, changing the current ICA to DRG shares might also mean a chance for real revenues. However, the attractiveness considerably depends on how the DRG shares are distributed to the different radiation disciplines of one institution.

Assessment of extracranial ICA stenosis with color ultrasound and CEMRA

International Nuclear Information System (INIS)

Zhao Wenyuan; Liu Jianmin; Xu Yi; Hong Bo; Huang Qinghai; Zhang Long; Zhou Xiaoping

2003-01-01

Objective: To evaluate the color ultrasound and CEMRA in assessment of extracranial ICA stenosis. Methods: The preoperation assessment of color ultrasound and CEMRA were reviewed in 93 cases who underwent interventional treatment for severe extracranial ICA stenosis. Results: Ultrasonic examination could reveal the nature and severity of the stenosis, while CEMRA could explore full length of carotid artery and find tandem stenosis. They both possessed a trend for overestimating the stenosis and could hardly show plaque ulceration. Conclusions: Up to the moment, neither color ultrasound nor CEMRA can substitute DSA. A combination of DSA, color ultrasound, and CEMRA could provide details of the stenotic ICA drawing an appropriate operation plan

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

Science.gov (United States)

George, S Thomas; Balakrishnan, R; Johnson, J Stanly; Jayakumar, J

2017-07-01

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

Science.gov (United States)

Sengottuvel, S; Khan, Pathan Fayaz; Mariyappa, N; Patel, Rajesh; Saipriya, S; Gireesan, K

2018-06-01

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

International Nuclear Information System (INIS)

Wang Wen-Bo; Zhang Xiao-Dong; Chang Yuchan; Wang Xiang-Li; Wang Zhao; Chen Xi; Zheng Lei

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. (paper)

Cross coherence independent component analysis in resting and action states EEG discrimination

International Nuclear Information System (INIS)

Almurshedi, A; Ismail, A K

2014-01-01

Cross Coherence time frequency transform and independent component analysis (ICA) method were used to analyse the electroencephalogram (EEG) signals in resting and action states during open and close eyes conditions. From the topographical scalp distributions of delta, theta, alpha, and beta power spectrum can clearly discriminate between the signal when the eyes were open or closed, but it was difficult to distinguish between resting and action states when the eyes were closed. In open eyes condition, the frontal area (Fp1, Fp2) was activated (higher power) in delta and theta bands whilst occipital (O1, O2) and partial (P3, P4, Pz) area of brain was activated alpha band in closed eyes condition. The cross coherence method of time frequency analysis is capable of discrimination between rest and action brain signals in closed eyes condition

Advanced technology components for model GTP305-2 aircraft auxiliary power system. Final report 6 May 75-15 Jul 79

Energy Technology Data Exchange (ETDEWEB)

Kidwell, J.R.; Large, G.D.

1980-02-01

The GTP305-2 Advanced APU is a single shaft, all shaft power engine incorporating an axial-centrifugal compressor, a reverse flow annular combustor and a radial-axial turbine. Cycle analyses indicated a 10-percent high pressure compressor flow increase improved matching characteristics with the low pressure compressor. The combustion system is a reverse flow annular combustor with an air-assist/airblast fuel injection system. The radial-axial turbine stage is characterized by an integrally cast turbine rotor and a cast exhaust duct assembly. The Integrated Components Assembly (ICA) rig consists of the combustor and turbines with a dummy mass on the shaft to simulate the compressor. ICA testing was conducted to establish component performance at design operating conditions. ICA and cold air aerodynamic testing of the turbine stage and cooling flow effects, indicates design efficiency goals were exceeded. ICA test results, cold-air testing and combustion system parameters were input to the cycle model. Room temperature strain-control LCF tests were performed and results analyzed on a Weibull distribution. Data analysis indicated LCF life improvement was obtained through HIP and heat treatment.

Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil.

Science.gov (United States)

Kassouf, Amine; El Rakwe, Maria; Chebib, Hanna; Ducruet, Violette; Rutledge, Douglas N; Maalouly, Jacqueline

2014-08-11

Olive oil is one of the most valued sources of fats in the Mediterranean diet. Its storage was generally done using glass or metallic packaging materials. Nowadays, plastic packaging has gained worldwide spread for the storage of olive oil. However, plastics are not inert and interaction phenomena may occur between packaging materials and olive oil. In this study, extra virgin olive oil samples were submitted to accelerated interaction conditions, in contact with polypropylene (PP) and polylactide (PLA) plastic packaging materials. 3D-front-face fluorescence spectroscopy, being a simple, fast and non destructive analytical technique, was used to study this interaction. Independent components analysis (ICA) was used to analyze raw 3D-front-face fluorescence spectra of olive oil. ICA was able to highlight a probable effect of a migration of substances with antioxidant activity. The signals extracted by ICA corresponded to natural olive oil fluorophores (tocopherols and polyphenols) as well as newly formed ones which were tentatively identified as fluorescent oxidation products. Based on the extracted fluorescent signals, olive oil in contact with plastics had slower aging rates in comparison with reference oils. Peroxide and free acidity values validated the results obtained by ICA, related to olive oil oxidation rates. Sorbed olive oil in plastic was also quantified given that this sorption could induce a swelling of the polymer thus promoting migration. Copyright © 2014 Elsevier B.V. All rights reserved.

Quantifying identifiability in independent component analysis

DEFF Research Database (Denmark)

Sokol, Alexander; Maathuis, Marloes H.; Falkeborg, Benjamin

2014-01-01

We are interested in consistent estimation of the mixing matrix in the ICA model, when the error distribution is close to (but different from) Gaussian. In particular, we consider $n$ independent samples from the ICA model $X = A\\epsilon$, where we assume that the coordinates of $\\epsilon......$ are independent and identically distributed according to a contaminated Gaussian distribution, and the amount of contamination is allowed to depend on $n$. We then investigate how the ability to consistently estimate the mixing matrix depends on the amount of contamination. Our results suggest...

The Research of Multiple Attenuation Based on Feedback Iteration and Independent Component Analysis

Science.gov (United States)

Xu, X.; Tong, S.; Wang, L.

2017-12-01

How to solve the problem of multiple suppression is a difficult problem in seismic data processing. The traditional technology for multiple attenuation is based on the principle of the minimum output energy of the seismic signal, this criterion is based on the second order statistics, and it can't achieve the multiple attenuation when the primaries and multiples are non-orthogonal. In order to solve the above problems, we combine the feedback iteration method based on the wave equation and the improved independent component analysis (ICA) based on high order statistics to suppress the multiple waves. We first use iterative feedback method to predict the free surface multiples of each order. Then, in order to predict multiples from real multiple in amplitude and phase, we design an expanded pseudo multi-channel matching filtering method to get a more accurate matching multiple result. Finally, we present the improved fast ICA algorithm which is based on the maximum non-Gauss criterion of output signal to the matching multiples and get better separation results of the primaries and the multiples. The advantage of our method is that we don't need any priori information to the prediction of the multiples, and can have a better separation result. The method has been applied to several synthetic data generated by finite-difference model technique and the Sigsbee2B model multiple data, the primaries and multiples are non-orthogonal in these models. The experiments show that after three to four iterations, we can get the perfect multiple results. Using our matching method and Fast ICA adaptive multiple subtraction, we can not only effectively preserve the effective wave energy in seismic records, but also can effectively suppress the free surface multiples, especially the multiples related to the middle and deep areas.

Entropy-based automated classification of independent components separated from fMCG

International Nuclear Information System (INIS)

Comani, S; Srinivasan, V; Alleva, G; Romani, G L

2007-01-01

Fetal magnetocardiography (fMCG) is a noninvasive technique suitable for the prenatal diagnosis of the fetal heart function. Reliable fetal cardiac signals can be reconstructed from multi-channel fMCG recordings by means of independent component analysis (ICA). However, the identification of the separated components is usually accomplished by visual inspection. This paper discusses a novel automated system based on entropy estimators, namely approximate entropy (ApEn) and sample entropy (SampEn), for the classification of independent components (ICs). The system was validated on 40 fMCG datasets of normal fetuses with the gestational age ranging from 22 to 37 weeks. Both ApEn and SampEn were able to measure the stability and predictability of the physiological signals separated with ICA, and the entropy values of the three categories were significantly different at p <0.01. The system performances were compared with those of a method based on the analysis of the time and frequency content of the components. The outcomes of this study showed a superior performance of the entropy-based system, in particular for early gestation, with an overall ICs detection rate of 98.75% and 97.92% for ApEn and SampEn respectively, as against a value of 94.50% obtained with the time-frequency-based system. (note)

Glucose & sodium chloride induced biofilm production & ica operon in clinical isolates of staphylococci

Directory of Open Access Journals (Sweden)

Astha Agarwal

2013-01-01

Full Text Available Background & objectives: All colonizing and invasive staphylococcal isolates may not produce biofilm but may turn biofilm producers in certain situations due to change in environmental factors. This study was done to test the hypothesis that non biofilm producing clinical staphylococci isolates turn biofilm producers in presence of sodium chloride (isotonic and high concentration of glucose, irrespective of presence or absence of ica operon. Methods: Clinical isolates of 100 invasive, 50 colonizing and 50 commensal staphylococci were tested for biofilm production by microtiter plate method in different culture media (trypticase soy broth alone or supplemented with 0.9% NaCl/ 5 or 10% glucose. All isolates were tested for the presence of ica ADBC genes by PCR. Results: Biofilm production significantly increased in the presence of glucose and saline, most, when both glucose and saline were used together. All the ica positive staphylococcal isolates and some ica negative isolates turned biofilm producer in at least one of the tested culture conditions. Those remained biofilm negative in different culture conditions were all ica negative. Interpretation & conclusions: The present results showed that the use of glucose or NaCl or combination of both enhanced biofilm producing capacity of staphylococcal isolates irrespective of presence or absence of ica operon.

PREFACE: 7th International Conference on Applied Electrostatics (ICAES-2012)

Science.gov (United States)

Li, Jie

2013-03-01

ICAES is an important conference organized every four years by the Committee on Electrostatics of the Chinese Physical Society, which serves as a forum for scientists, educators and engineers interested in the fundamentals, applications, disasters and safety of electrostatics, etc. In recent years, new techniques, applications and fundamental theories on electrostatics have developed considerably. ICAES-7, held in Dalian, China, from 17-19 September 2012, aimed to provide a forum for all scholars to report the newest developments in electrostatics, to probe the questions that scholars faced and to discuss fresh ideas related to electrostatics. ICAES-7 was co-organized and hosted by Dalian University of Technology, and was sponsored by the Ministry of Education of China, the National Natural Science Foundation of China, Dalian University of Technology, Nanjing Suman Electronics Co. Ltd (Suman, China), Shekonic (Yangzhou Shuanghong, China) Electric/Mechanical Co. Ltd, and Suzhou TA&A Ultra Clean Technology Co. Ltd. (China). On behalf of the organizing committee of ICAES-7, I express my great appreciation for their support of the conference. Over 160 scholars and engineers from many countries including Croatia, The Czech Republic, D.P.R. Korea, Germany, Japan, Malaysia, Poland, Russia, the United States of America, China attended ICAES-7, and the conference collected and selected 149 papers for publication. The subjects of those papers cover the fundamentals of electrostatics, electrostatic disaster and safety, and electrostatic application (e.g. precipitation, pollutant control, biological treatment, mixture separation and food processing, etc). I cordially thank all authors and attendees for their support, and my appreciation is also given to the conference honorary chair, the organizing committee and advisory committee, and the conference secretaries for their hard work. ICAES-7 is dedicated to the memory of Professor Jen-Shih Chang (professor emeritus in the

The ICA Communication Audit: Process, Status, Critique

Science.gov (United States)

Goldhaber, Gerald M.; Krivonos, Paul D.

1977-01-01

Explores the International Communication Association (ICA) Audit process including goals, products, instruments, audit logistics and timetable, feedback of results and follow-up, costs, current status and audits conducted to date. (ED.)

The role of biofilms in persistent infections and factors involved in ica-independent biofilm development and gene regulation in Staphylococcus aureus.

Science.gov (United States)

Figueiredo, Agnes Marie Sá; Ferreira, Fabienne Antunes; Beltrame, Cristiana Ossaille; Côrtes, Marina Farrel

2017-09-01

Staphylococcus aureus biofilms represent a unique micro-environment that directly contribute to the bacterial fitness within hospital settings. The accumulation of this structure on implanted medical devices has frequently caused the development of persistent and chronic S. aureus-associated infections, which represent an important social and economic burden worldwide. ica-independent biofilms are composed of an assortment of bacterial products and modulated by a multifaceted and overlapping regulatory network; therefore, biofilm composition can vary among S. aureus strains. In the microniches formed by biofilms-produced by a number of bacterial species and composed by different structural components-drug refractory cell subpopulations with distinct physiological characteristics can emerge and result in therapeutic failures in patients with recalcitrant bacterial infections. In this review, we highlight the importance of biofilms in the development of persistence and chronicity in some S. aureus diseases, the main molecules associated with ica-independent biofilm development and the regulatory mechanisms that modulate ica-independent biofilm production, accumulation, and dispersion.

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

Science.gov (United States)

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Weak effect of metal type and ica genes on staphylococcal infection of titanium and stainless steel implants.

Science.gov (United States)

Hudetz, D; Ursic Hudetz, S; Harris, L G; Luginbühl, R; Friederich, N F; Landmann, R

2008-12-01

Currently, ica is considered to be the major operon responsible for staphylococcal biofilm. The effect of biofilm on susceptibility to staphylococcal infection of different implant materials in vivo is unclear. The interaction of ica-positive (wild-type (WT)) and ica-negative (ica(-)) Staphylococcus aureus and Staphylococcus epidermidis strains with titanium and both smooth and rough stainless steel surfaces was studied by scanning electron microscopy in vitro and in a mouse tissue cage model during 2 weeks following perioperative or postoperative inoculation in vivo. In vitro, WT S. epidermidis adhered equally and more strongly than did WT S. aureus to all materials. Both WT strains, but not ica(-) strains, showed multilayered biofilm. In vivo, 300 CFUs of WT and ica(-)S. aureus led, in all metal cages, to an infection with a high level of planktonic CFUs and only 0.89% adherent CFUs after 8 days. In contrast, 10(6) CFUs of the WT and ica(-) strains were required for postoperative infection with S. epidermidis. In all metal types, planktonic numbers of S. epidermidis dropped to titanium cages adherent WT bacteria survived in higher numbers than ica(-) bacteria. In conclusion, the metal played a minor role in susceptibility to and persistence of staphylococcal infection; the presence of ica genes had a strong effect on biofilm in vitro and a weak effect in vivo; and S. epidermidis was more pathogenic when introduced during implantation than after implantation.

New insight on the water management in Ica Valley-Peru

Science.gov (United States)

Guttman, Joseph; Berger, Diego

2014-05-01

The Andes divide Peru into three natural drainage basins: Pacific basin, Atlantic basin and Lake Titicaca basin. According to the National Water Authority (ANA), the Pacific basin is the driest basin. The bulk of water that feed the local aquifers in the coastal Pacific region is coming from rivers that flow west from the Andes. One of them is the Ica River- source of the Ica Aquifer and the Pampas de Villacuri Aquifer. The Ica River flows in a graben that was created by a series of faults. The graben is filled with sand and gravel with interbeded and lenses of clay. The aquifer thickness varies between 25 meters to more than 200 meters. The Ica Valley has an extension of 7700 km2 and belongs to the Province of Ica, the second larger economic center in Peru. The Valley is located in the hyperarid region of the Southern Coastal area of Peru with a few millimeters of precipitation per year. The direct recharge is almost zero. The recharge into the Ica Valley aquifer is comes indirectly by infiltration of storm water through the riverbed generates in the Andes, through irrigation canals and by irrigation return flow. In this hyperarid region, local aquifers like the Ica Valley are extremely valuable resources to local populations and are the key sources of groundwater for agriculture and population needs. Therefore, these aquifers play a crucial role in providing people with water and intense attention should be given to manage the water sector properly and to keep the aquifer sustainable for future generations. The total pumping (from rough estimations) is much greater than the direct and indirect recharge. The deficit in the water balance is reflected in large water level decline, out of operation of shallow wells and the ascending of saline water from deeper layers. The change from flood irrigation that contributes about 35-40% of the water to the aquifer, to drip irrigation dramatically reduces the amount of water that infiltrates into the sub-surface from the

A comparison between PCR and Immunochromatography assay (ICA in diagnosis of hemorrhagic gastroenteritis caused by Canine parvovirus

Directory of Open Access Journals (Sweden)

Vakili , N.

2014-05-01

Full Text Available Canine parvovirus type 2 (CPV-2 is one of the most common viruses responsible for acute hemorrhagic enteritis in dogs. A rapid and accurate diagnosis of CPV-2 infection is especially important in kennels in order to isolate infected dogs. The aim of the present study was to compare two laboratory tests i.e., Polymerase Change Reaction (PCR and Immunochromatography assay (ICA most commonly used for the diagnosis of canine parvovirus infection in companion dogs. Fecal samples were collected from fifty five dogs (50=hemorrhagic diarrheic and 5= healthy between 2011 and 2012 in Ahvaz district, southwest of Iran. The studied dogs were divided into two age groups (6 months, four different breeds (Terriers, German shepherds, Doberman pinschers and Mixed and based on environment into two groups (open and close also. All samples were tested by ICA and PCR methods and the results were analyzed by using Kappa test, Mc Nemar and Chi-square analysis. ICA and PCR were able to detect CPV-2 antigen or nucleic acid in 33 and 50 of the hemorrhagic diarrheic samples, respectively. Samples of healthy dogs were negative by both tests. Although sensitivity of ICA compared with PCR method was determined to be 66% (PCR more sensitive than ICA, nevertheless statistical analysis showed that the difference between two techniques were not significant (P>0.05. Kappa test was obtained 0.38 between two techniques. CBC showed that most infected dogs had leucopenia, lymphopenia and neutropenia also (82%; 41 out of 50 samples.Obtained results of this survey showed that accurate standardization of laboratory tests is required to provide veterinarian with an effective tool for a precise etiological diagnosis of hemorrhagic gastroenteritis due to CPV infection. Although Immunochromatography is a simple and quick method for screening of fecal samples of dogs suspected of CPV infection, but PCR is more sensitive and reliable than ICA. Moreover, the subtypes of the virus determined by

Clinical significance of detection of GAD-Ab, ICA and IAA in patients with diabetes mellitus

International Nuclear Information System (INIS)

Zhou Jinhong; Liu Zhenzong; Wang Huimin

2006-01-01

To explore value of combined detection of glutamic acid decarboxylase antibody(GAD-Ab), islet cell antibody (ICA) and insulin autoantibody (IAA) in patients with diabetes mellitus (DM). Serum GAD-Ab, ICA and IAA were detected by chemiluminescent immunoassay and RIA in 46 of type 1 diabetes mellitus (1 DM), 78 of 2 DM respectively, and in 50 nondiabetic subjects as control group, and analysed according to the positive rates of different groups. Results showed that the positive rate of GAD-Ab was 67.39%, ICA 39.73%, IAA 23.91% in 1 DM,and that of GAD-Ab was 8.97%, ICA 15.39%, IAA 10.26% in 2 DM respectively. The positive rate of combined detection of GAD-Ab, ICA and IAA was 91.30% in patients with 1 DM, and 29.49% which was higher than that in control group (P<0.01). The detection of serum GAD-Ab,ICA and IAA might be regarded as clinical significance for classification, treatment and predict prognosis of DM. (authors)

Attribution of emotions to body postures: an independent component analysis study of functional connectivity in autism.

Science.gov (United States)

Libero, Lauren E; Stevens, Carl E; Kana, Rajesh K

2014-10-01

The ability to interpret others' body language is a vital skill that helps us infer their thoughts and emotions. However, individuals with autism spectrum disorder (ASD) have been found to have difficulty in understanding the meaning of people's body language, perhaps leading to an overarching deficit in processing emotions. The current fMRI study investigates the functional connectivity underlying emotion and action judgment in the context of processing body language in high-functioning adolescents and young adults with autism, using an independent components analysis (ICA) of the fMRI time series. While there were no reliable group differences in brain activity, the ICA revealed significant involvement of occipital and parietal regions in processing body actions; and inferior frontal gyrus, superior medial prefrontal cortex, and occipital cortex in body expressions of emotions. In a between-group analysis, participants with autism, relative to typical controls, demonstrated significantly reduced temporal coherence in left ventral premotor cortex and right superior parietal lobule while processing emotions. Participants with ASD, on the other hand, showed increased temporal coherence in left fusiform gyrus while inferring emotions from body postures. Finally, a positive predictive relationship was found between empathizing ability and the brain areas underlying emotion processing in ASD participants. These results underscore the differential role of frontal and parietal brain regions in processing emotional body language in autism. Copyright © 2014 Wiley Periodicals, Inc.

Micro-bunching diagnostics for the ICA by coherent transition radiation

International Nuclear Information System (INIS)

Liu, Y.; Bogacz, S.A.; Cline, D.B.; Wang, X.J.; Pogorelsky, I.V.; Kimura, W.D.

1995-01-01

Here, the authors propose an effective method to detect micro-bunching effects (10 fs bunch length), produced by the ICA interaction, by using the CTR spectrum. The re-bunching of initially energy modulated e-beam passing through a Hydrogen gas cell (ICA interaction) is studied via a Monte Carlo simulation code (STI), as well as in a space-charge dominated region by a multi-particle time domain tracking code (PARMELA). The results show that even in a strong space-charge dominated region the re-bunching effect is still very pronounced. The erosion of bunching due to the space-charge defocusing washes out the final bunching peak only by about 10% (FWHM). The longitudinal distribution of a micro-bunched beam is Fourier analyzed to find the dominant harmonics contributing to the CTR. The CTR spectrum is calculated analytically for the ICA situation. A schematic of the experimental set up is also proposed

Sea level reconstructions from altimetry and tide gauges using independent component analysis

Science.gov (United States)

Brunnabend, Sandra-Esther; Kusche, Jürgen; Forootan, Ehsan

2017-04-01

Many reconstructions of global and regional sea level rise derived from tide gauges and satellite altimetry used the method of empirical orthogonal functions (EOF) to reduce noise, improving the spatial resolution of the reconstructed outputs and investigate the different signals in climate time series. However, the second order EOF method has some limitations, e.g. in the separation of individual physical signals into different modes of sea level variations and in the capability to physically interpret the different modes as they are assumed to be orthogonal. Therefore, we investigate the use of the more advanced statistical signal decomposition technique called independent component analysis (ICA) to reconstruct global and regional sea level change from satellite altimetry and tide gauge records. Our results indicate that the used method has almost no influence on the reconstruction of global mean sea level change (1.6 mm/yr from 1960-2010 and 2.9 mm/yr from 1993-2013). Only different numbers of modes are needed for the reconstruction. Using the ICA method is advantageous for separating independent climate variability signals from regional sea level variations as the mixing problem of the EOF method is strongly reduced. As an example, the modes most dominated by the El Niño-Southern Oscillation (ENSO) signal are compared. Regional sea level changes near Tianjin, China, Los Angeles, USA, and Majuro, Marshall Islands are reconstructed and the contributions from ENSO are identified.

Oligocene sharks from vicinity of Poljšica near Podnart, Slovenia

Directory of Open Access Journals (Sweden)

Vasja Mikuž

2014-12-01

Full Text Available Poljšica and surroundings are known for numerous interesting fossil remains. Among the rarest fossil remains are vertebrates, and in the Poljšica Oligocene these consist exclusively of fish remains. In this contribution are considered six teeth of sharks, five belonging to species Carcharias cuspidatus (Agassiz, 1843, except a single problematic tooth that can be attributed most probably to genus Cosmopolitodus.

Ica-expression and gentamicin susceptibility of Staphylococcus epidermidis biofilm on orthopedic implant biomaterials

NARCIS (Netherlands)

Nuryastuti, Titik; Krom, Bastiaan P.; Aman, Abu T.; Busscher, Henk J.; van der Mei, Henny C.

Ica-expression by Staphylococcus epidermidis and slime production depends on environmental conditions such as implant material and presence of antibiotics. Here, we evaluate biofilm formation and ica-expression of S. epidermidis strains on biomaterials involved in total hip-and knee arthroplasty

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

Directory of Open Access Journals (Sweden)

Balbir Singh

2017-01-01

Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

Science.gov (United States)

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

Separating complex compound patient motion tracking data using independent component analysis

Science.gov (United States)

Lindsay, C.; Johnson, K.; King, M. A.

2014-03-01

In SPECT imaging, motion from respiration and body motion can reduce image quality by introducing motion-related artifacts. A minimally-invasive way to track patient motion is to attach external markers to the patient's body and record their location throughout the imaging study. If a patient exhibits multiple movements simultaneously, such as respiration and body-movement, each marker location data will contain a mixture of these motions. Decomposing this complex compound motion into separate simplified motions can have the benefit of applying a more robust motion correction to the specific type of motion. Most motion tracking and correction techniques target a single type of motion and either ignore compound motion or treat it as noise. Few methods account for compound motion exist, but they fail to disambiguate super-position in the compound motion (i.e. inspiration in addition to body movement in the positive anterior/posterior direction). We propose a new method for decomposing the complex compound patient motion using an unsupervised learning technique called Independent Component Analysis (ICA). Our method can automatically detect and separate different motions while preserving nuanced features of the motion without the drawbacks of previous methods. Our main contributions are the development of a method for addressing multiple compound motions, the novel use of ICA in detecting and separating mixed independent motions, and generating motion transform with 12 DOFs to account for twisting and shearing. We show that our method works with clinical datasets and can be employed to improve motion correction in single photon emission computed tomography (SPECT) images.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

Science.gov (United States)

Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

2012-09-01

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are

Recurrence of ICA-PCoA aneurysms after neck clipping.

Science.gov (United States)

Sakaki, T; Takeshima, T; Tominaga, M; Hashimoto, H; Kawaguchi, S

1994-01-01

Between 1975 and 1992, 2211 patients underwent aneurysmal neck clipping at the Nara Medical University clinic and associated hospitals. The aneurysm in 931 of these patients was situated at the junction of the internal carotid artery (ICA) and posterior communicating artery (PCoA). Seven patients were readmitted 4 to 17 years after the first surgery because of regrowth and rupture of an ICA-PCoA aneurysmal sac that had arisen from the residual neck. On angiograms obtained following aneurysmal neck clipping, a large primitive type of PCoA was demonstrated in six patients and a small PCoA in one. A small residual aneurysm was confirmed in only two patients and angiographically complete neck clipping in five. Recurrent ICA-PCoA aneurysms were separated into two types based on the position of the old clip in relation to the new growth. Type 1 aneurysms regrow from the entire neck and balloon eccentrically. In this type, it is possible to apply the clip at the neck as in conventional clipping for a ruptured aneurysm. Type 2 includes aneurysms in which the proximal portion of a previous clip is situated at the corner of the ICA and aneurysmal neck and the distal portion on the enlarged dome of the aneurysm, because the sac is regrowing from a portion of the residual neck. In this type of aneurysm, a Sugita fenestrated clip can occlude the residual neck, overriding the old clip. Classifying these aneurysms into two groups is very useful from a surgical point of view because it is possible to apply a new clip without removing the old clip, which was found to be adherent to surrounding tissue.

Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

Science.gov (United States)

Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

2016-04-29

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders

Science.gov (United States)

Du, Yuhui; Pearlson, Godfrey D; Liu, Jingyu; Sui, Jing; Yu, Qingbao; He, Hao; Castro, Eduardo; Calhoun, Vince D.

2015-01-01

Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. The present study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering SAD with manic episodes (SADM), and 13 patients suffering SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering methods were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly include frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

Science.gov (United States)

Delorme, Arnaud; Makeig, Scott

2004-03-15

We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.

Emotional responses as independent components in EEG

DEFF Research Database (Denmark)

Jensen, Camilla Birgitte Falk; Petersen, Michael Kai; Larsen, Jakob Eg

2014-01-01

susceptible to noise if captured in a mobile context. Hypothesizing that retrieval of emotional responses in mobile usage scenarios could be enhanced through spatial filtering, we compare a standard EEG electrode based analysis against an approach based on independent component analysis (ICA). By clustering...... or unpleasant images; early posterior negativity (EPN) and late positive potential (LPP). Recent studies suggest that several time course components may be modulated by emotional content in images or text. However these neural signatures are characterized by small voltage changes that would be highly...... by emotional content. We propose that similar approaches to spatial filtering might allow us to retrieve more robust signals in real life mobile usage scenarios, and potentially facilitate design of cognitive interfaces that adapt the selection of media to our emotional responses....

Separating spectral mixtures in hyperspectral image data using independent component analysis: validation with oral cancer tissue sections

Science.gov (United States)

Duann, Jeng-Ren; Jan, Chia-Ing; Ou-Yang, Mang; Lin, Chia-Yi; Mo, Jen-Feng; Lin, Yung-Jiun; Tsai, Ming-Hsui; Chiou, Jin-Chern

2013-12-01

Recently, hyperspectral imaging (HSI) systems, which can provide 100 or more wavelengths of emission autofluorescence measures, have been used to delineate more complete spectral patterns associated with certain molecules relevant to cancerization. Such a spectral fingerprint may reliably correspond to a certain type of molecule and thus can be treated as a biomarker for the presence of that molecule. However, the outcomes of HSI systems can be a complex mixture of characteristic spectra of a variety of molecules as well as optical interferences due to reflection, scattering, and refraction. As a result, the mixed nature of raw HSI data might obscure the extraction of consistent spectral fingerprints. Here we present the extraction of the characteristic spectra associated with keratinized tissues from the HSI data of tissue sections from 30 oral cancer patients (31 tissue samples in total), excited at two different wavelength ranges (330 to 385 and 470 to 490 nm), using independent and principal component analysis (ICA and PCA) methods. The results showed that for both excitation wavelength ranges, ICA was able to resolve much more reliable spectral fingerprints associated with the keratinized tissues for all the oral cancer tissue sections with significantly higher mean correlation coefficients as compared to PCA (p<0.001).

Lateral supraorbital approach to ipsilateral PCA-P1 and ICA-PCoA aneurysms.

Science.gov (United States)

Goehre, Felix; Jahromi, Behnam Rezai; Elsharkawy, Ahmed; Lehto, Hanna; Shekhtman, Oleg; Andrade-Barazarte, Hugo; Munoz, Francisco; Hijazy, Ferzat; Makhkamov, Makhkam; Hernesniemi, Juha

2015-01-01

Aneurysms of the posterior cerebral artery (PCA) are rare and often associated with anterior circulation aneurysms. The lateral supraorbital approach allows for a very fast and safe approach to the ipsilateral lesions Circle of Willis. A technical note on the successful clip occlusion of two aneurysms in the anterior and posterior Circle of Willis via this less invasive approach has not been published before. The objective of this technical note is to describe the simultaneous microsurgical clip occlusion of an ipsilateral PCA-P1 and an internal carotid artery - posterior communicating artery (ICA-PCoA) aneurysm via the lateral supraorbital approach. The authors present a technical report of successful clip occlusions of ipsilateral located PCA-P1 and ICA-PCoA aneurysms. A 59-year-old female patient was diagnosed with a PCA-P1 and an ipsilateral ICA-PCoA aneurysm by computed tomography angiography (CTA) after an ischemic stroke secondary to a contralateral ICA dissection. The patient underwent microsurgical clipping after a lateral supraorbital craniotomy. The intraoperative indocyanine green (ICG) videoangiography and the postoperative CTA showed a complete occlusion of both aneurysms; the parent vessels (ICA and PCA) were patent. The patient presents postoperative no new neurologic deficit. The lateral supraorbital approach is suitable for the simultaneous microsurgical treatment of proximal anterior circulation and ipsilateral proximal PCA aneurysms. Compared to endovascular treatment, direct visual control of brainstem perforators is possible.

Robust spinal cord resting-state fMRI using independent component analysis-based nuisance regression noise reduction.

Science.gov (United States)

Hu, Yong; Jin, Richu; Li, Guangsheng; Luk, Keith Dk; Wu, Ed X

2018-04-16

Physiological noise reduction plays a critical role in spinal cord (SC) resting-state fMRI (rsfMRI). To reduce physiological noise and increase the robustness of SC rsfMRI by using an independent component analysis (ICA)-based nuisance regression (ICANR) method. Retrospective. Ten healthy subjects (female/male = 4/6, age = 27 ± 3 years, range 24-34 years). 3T/gradient-echo echo planar imaging (EPI). We used three alternative methods (no regression [Nil], conventional region of interest [ROI]-based noise reduction method without ICA [ROI-based], and correction of structured noise using spatial independent component analysis [CORSICA]) to compare with the performance of ICANR. Reduction of the influence of physiological noise on the SC and the reproducibility of rsfMRI analysis after noise reduction were examined. The correlation coefficient (CC) was calculated to assess the influence of physiological noise. Reproducibility was calculated by intraclass correlation (ICC). Results from different methods were compared by one-way analysis of variance (ANOVA) with post-hoc analysis. No significant difference in cerebrospinal fluid (CSF) pulsation influence or tissue motion influence were found (P = 0.223 in CSF, P = 0.2461 in tissue motion) in the ROI-based (CSF: 0.122 ± 0.020; tissue motion: 0.112 ± 0.015), and Nil (CSF: 0.134 ± 0.026; tissue motion: 0.124 ± 0.019). CORSICA showed a significantly stronger influence of CSF pulsation and tissue motion (CSF: 0.166 ± 0.045, P = 0.048; tissue motion: 0.160 ± 0.032, P = 0.048) than Nil. ICANR showed a significantly weaker influence of CSF pulsation and tissue motion (CSF: 0.076 ± 0.007, P = 0.0003; tissue motion: 0.081 ± 0.014, P = 0.0182) than Nil. The ICC values in the Nil, ROI-based, CORSICA, and ICANR were 0.669, 0.645, 0.561, and 0.766, respectively. ICANR more effectively reduced physiological noise from both tissue motion and CSF pulsation than three alternative methods. ICANR increases the robustness of SC rsf

Animas Altas' “Friezes Building”. Being Paracas At The Lower Ica Valley

OpenAIRE

Bachir Bacha, Aïcha

2017-01-01

This paper presents and discusses the results of recent investigations carried out by the Ánimas Altas Archaeological Program, in the Ica department, southern Peru. A particular emphasis is put on the excavations conducted at a frieze-decorated building. The analysis of the material culture registered in this building, particularly of the icons depicted on the friezes, not only offers some interpretations regarding Paracas symbology and cosmovision but also sheds lights on critical aspects re...

Detection of Intracellular Adhesion (ica and Biofilm Formation Genes in Staphylococcus aureus Isolates from Clinical Samples

Directory of Open Access Journals (Sweden)

Khadije Rezaie Keikhaie

2017-02-01

Full Text Available Introduction: Nosocomial infections that result in the formation of biofilms on the surfaces of biomedical implants are a leading cause of sepsis and are often associated with colonization of the implants by Staphylococcus epidermidis. Biofilm formation is thought to require two sequential steps: adhesion of cells to a solid substrate followed by cell-cell adhesion, creating multiple layers of cells. Intercellular adhesion requires the polysaccharide intercellular adhesion (PIA, which is composed of linear β-1, 6-linked glucosaminylglycans and can be synthesized in vitro from UDP-N-acetylglucosamine by products of the intercellular adhesion (ica locus. We have investigated a variety of Staphylococcus aureus strains and find that all strains tested contain the ica locus and that several can form biofilms in vitro. Material and Method: A total of 31 clinical S. aureus isolates were collected from Zabol, Iran. In vitro biofilm formation ability was determined by microliter tissue culture plates. All clinical isolates were examined for determination the ica locus by using PCR method. Result: The results of this study showed that 40 strains of Staphylococcus aureus, 12 strains carrying the gene Cocos icaA (30% and 8 strains carrying the gene icaD (20% and the number of five strains (12.5% containing both genes ica A and has been ica D. Conclusions:  S. aureus clinical isolates have different ability to form biofilm. This may be caused by the differences in the expression of biofilm related genes, genetic make-up and physiological conditions.

Traumatic pseudoaneurysm of the intracavernous ICA presenting with massive epistaxis: imaging diagnosis and endovascular treatment.

Science.gov (United States)

Han, M H; Sung, M W; Chang, K H; Min, Y G; Han, D H; Han, M C

1994-03-01

Traumatic pseudoaneurysm of the intracavernous internal carotid artery (ICA) is a very rare cause of epistaxis but is a life-threatening clinical situation when left untreated. The authors have experienced four cases of traumatic pseudoaneurysm involving the intracavernous ICA. Delayed massive epistaxes developed 1 to 8 months after trauma and initial transient epistaxis in all four patients. Three of the cases were successfully managed by the detachable balloon occlusion (DBO) of the ICA along with the aneurysm openings. In one case, a large pseudoaneurysm destroying a large area of the central skull base with peripheral blood clot was demonstrated on computed tomography, magnetic resonance imaging, and angiography; this patient died due to massive epistaxis before the trial of DBO. Imaging findings of pseudoaneurysms involving the intracavernous ICA in the four cases are described, and the role of endovascular treatment is discussed.

Separation of GRACE geoid time-variations using Independent Component Analysis

Science.gov (United States)

Frappart, F.; Ramillien, G.; Maisongrande, P.; Bonnet, M.

2009-12-01

Independent Component Analysis (ICA) is a blind separation method based on the simple assumptions of the independence of the sources and the non-Gaussianity of the observations. An approach based on this numerical method is used here to extract hydrological signals over land and oceans from the polluting striping noise due to orbit repetitiveness and present in the GRACE global mass anomalies. We took advantage of the availability of monthly Level-2 solutions from three official providers (i.e., CSR, JPL and GFZ) that can be considered as different observations of the same phenomenon. The efficiency of the methodology is first demonstrated on a synthetic case. Applied to one month of GRACE solutions, it allows to clearly separate the total water storage change from the meridional-oriented spurious gravity signals on the continents but not on the oceans. This technique gives results equivalent as the destriping method for continental water storage for the hydrological patterns with less smoothing. This methodology is then used to filter the complete series of the 2002-2009 GRACE solutions.

Characterizing functional connectivity during rest in multiple sclerosis patients versus healthy volunteers using independent component analysis

Energy Technology Data Exchange (ETDEWEB)

Palacio Garcia, L.; Andrzejak, R.; Prchkovska, V.; Rodrigues, P.

2016-07-01

It is commonly thought that our brain is not active when it does not receive any external input. However, during rest, there are still certain distant regions of the brain that are functionally correlated between them: the so-called resting-state networks. This functional connectivity of the brain is disrupted in many neurological diseases. In particular, it has been shown that one of the most studied resting-state networks (the default-mode network) is affected in multiple sclerosis, which is the most common disabling neurological condition affecting the central nervous system of young adults. In this work, I focus on the study of the differences in the resting-state networks between multiple sclerosis patients and healthy volunteers. In order to study the effects of multiple sclerosis on the functional connectivity of the brain, a numerical method known as independent component analysis (ICA) is applied. This technique divides the resting-state fMRI data into independent components. Nonetheless, noise, which could be due to head motion or physiological artifacts, may corrupt the data by indicating a false activation. Therefore, I create a web user interface that allows the user to manually classify all the independent components for a given subject. Eventually, the components classified as noise should be removed from the functional data in order to prevent them from taking part in any further analysis. (Author)

Comparison report of RPV pressurised thermal shock - international comparative assessment study (PTS ICAS)

International Nuclear Information System (INIS)

1999-01-01

A summary of the recently completed International Comparative Assessment Study of Pressurized- Thermal-Shock in Reactor Pressure Vessels (RPV PTS ICAS) is presented here to record the results in actual and comparative fashions. The ICAS Project brought together an international group of experts from research, utility and regulatory organizations to perform a comparative evaluation of analysis methodologies employed in the assessment of RPV integrity under PTS loading conditions. The Project was sponsored jointly by Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS), Koeln, Germany, and Oak Ridge National Laboratory (ORNL), USA, with assistance from the Organization for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA)/Committee on the Safety of Nuclear Installations (CSNI)/Principal Working Group (PWG) No. 3 (Integrity of Components and Structures). The ICAS Project grew out of a strong interest expressed by participants in the previous FALSIRE II Project to proceed with further evaluations of analysis methods used in RPV integrity assessment. A Launch Meeting for the ICAS Project was held at GRS-Koeln, during June 1996, where an emphasis was placed on identifying the different approaches to RPV integrity assessment being employed within the international nuclear technology community. Also a Problem Statement was drafted that defined a Western type four-loop RPV with cladding on the inner surface. Also, a detailed task matrix was defined that included a set of transient thermal-mechanical loading conditions postulated to result from loss-of-coolant accidents. The primary focus of the analyses was on the behaviour of relatively shallow cracks under these conditions. The assessment activities based on the Problem Statement were divided under three tasks: deterministic fracture mechanics (DFM), probabilistic fracture mechanics (PFM) and thermal-hydraulic mixing (THM). An Intermediate Workshop was held at OECD/NEA-Paris during June 1997, to

Stabilizing bidirectional associative memory with Principles in Independent Component Analysis and Null Space (PICANS)

Science.gov (United States)

LaRue, James P.; Luzanov, Yuriy

2013-05-01

A new extension to the way in which the Bidirectional Associative Memory (BAM) algorithms are implemented is presented here. We will show that by utilizing the singular value decomposition (SVD) and integrating principles of independent component analysis (ICA) into the nullspace (NS) we have created a novel approach to mitigating spurious attractors. We demonstrate this with two applications. The first application utilizes a one-layer association while the second application is modeled after the several hierarchal associations of ventral pathways. The first application will detail the way in which we manage the associations in terms of matrices. The second application will take what we have learned from the first example and apply it to a cascade of a convolutional neural network (CNN) and perceptron this being our signal processing model of the ventral pathways, i.e., visual systems.

SVM-based glioma grading. Optimization by feature reduction analysis

International Nuclear Information System (INIS)

Zoellner, Frank G.; Schad, Lothar R.; Emblem, Kyrre E.; Harvard Medical School, Boston, MA; Oslo Univ. Hospital

2012-01-01

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∝87%) while reducing the number of features by up to 98%. (orig.)

SVM-based glioma grading. Optimization by feature reduction analysis

Energy Technology Data Exchange (ETDEWEB)

Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

2012-11-01

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

Science.gov (United States)

Hosseini, K.; Ayati, Z.; Ansari, R.

2018-04-01

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd-Bullough-Mikhailov and Tzitzéica-Dodd-Bullough equations, are obtained using the expa function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.

Independent component analysis of high-resolution imaging data identifies distinct functional domains

DEFF Research Database (Denmark)

Reidl, Juergen; Starke, Jens; Omer, David

2007-01-01

be automatically detected. In the visual cortex orientation columns can be extracted. In all cases artifacts due to movement, heartbeat or respiration were separated from the functional signal by sICA and could be removed from the data set. sICA is therefore a powerful technique for data compression, unbiased...

Multi-Temporal Independent Component Analysis and Landsat 8 for Delineating Maximum Extent of the 2013 Colorado Front Range Flood

Directory of Open Access Journals (Sweden)

Stephen M. Chignell

2015-07-01

Full Text Available Maximum flood extent—a key data need for disaster response and mitigation—is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI and Independent Component Analysis (ICA for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre- and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA

Detection of gear cracks in a complex gearbox of wind turbines using supervised bounded component analysis of vibration signals collected from multi-channel sensors

Science.gov (United States)

Li, Zhixiong; Yan, Xinping; Wang, Xuping; Peng, Zhongxiao

2016-06-01

In the complex gear transmission systems, in wind turbines a crack is one of the most common failure modes and can be fatal to the wind turbine power systems. A single sensor may suffer with issues relating to its installation position and direction, resulting in the collection of weak dynamic responses of the cracked gear. A multi-channel sensor system is hence applied in the signal acquisition and the blind source separation (BSS) technologies are employed to optimally process the information collected from multiple sensors. However, literature review finds that most of the BSS based fault detectors did not address the dependence/correlation between different moving components in the gear systems; particularly, the popular used independent component analysis (ICA) assumes mutual independence of different vibration sources. The fault detection performance may be significantly influenced by the dependence/correlation between vibration sources. In order to address this issue, this paper presents a new method based on the supervised order tracking bounded component analysis (SOTBCA) for gear crack detection in wind turbines. The bounded component analysis (BCA) is a state of art technology for dependent source separation and is applied limitedly to communication signals. To make it applicable for vibration analysis, in this work, the order tracking has been appropriately incorporated into the BCA framework to eliminate the noise and disturbance signal components. Then an autoregressive (AR) model built with prior knowledge about the crack fault is employed to supervise the reconstruction of the crack vibration source signature. The SOTBCA only outputs one source signal that has the closest distance with the AR model. Owing to the dependence tolerance ability of the BCA framework, interfering vibration sources that are dependent/correlated with the crack vibration source could be recognized by the SOTBCA, and hence, only useful fault information could be preserved in

Dynamic in vivo mutations within the ica operon during persistence of Staphylococcus aureus in the airways of cystic fibrosis patients.

Directory of Open Access Journals (Sweden)

Bianca Schwartbeck

2016-11-01

Full Text Available Cystic fibrosis (CF is associated with chronic bacterial airway infections leading to lung insufficiency and decreased life expectancy. Staphylococcus aureus is one of the most prevalent pathogens isolated from the airways of CF patients. Mucoid colony morphology has been described for Pseudomonas aeruginosa, the most common pathogen in CF, but not for S. aureus. From the airways of 8 of 313 CF patients (2.5% mucoid S. aureus isolates (n = 115 were cultured with a mean persistence of 29 months (range 1 month, 126 months. In contrast to non-mucoid S. aureus, mucoid isolates were strong biofilm formers. The upstream region of the ica operon, which encodes the proteins responsible for the synthesis of the polysaccharide intercellular adhesin (PIA, of mucoid isolates was sequenced. Spa-types of mucoid and non-mucoid strains were identical, but differed between patients. Mucoid isolates carried a 5 bp deletion in the intergenic region between icaR and icaA. During long-term persistence, from two patients subsequent non-mucoid isolates (n = 12 with 5 bp deletions were cultured, which did not produce biofilm. Sequencing of the entire ica operon identified compensatory mutations in various ica-genes including icaA (n = 7, icaD (n = 3 and icaC (n = 2. Six sequential isolates of each of these two patients with non-mucoid and mucoid phenotypes were subjected to whole genome sequencing revealing a very close relationship of the individual patient's isolates. Transformation of strains with vectors expressing the respective wild-type genes restored mucoidy. In contrast to the non-mucoid phenotype, mucoid strains were protected against neutrophilic killing and survived better under starvation conditions. In conclusion, the special conditions present in CF airways seem to facilitate ongoing mutations in the ica operon during S. aureus persistence.

Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution.

Science.gov (United States)

Govindan, R B; Kota, Srinivas; Al-Shargabi, Tareq; Massaro, An N; Chang, Taeun; du Plessis, Adre

2016-09-01

Electroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG. We propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG. The null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG. We show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity. Our analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

International Conference on Applied Sciences (ICAS2013)

Science.gov (United States)

Lemle, Ludovic Dan; Jiang, Yiwen

2014-03-01

The International Conference on Applied Sciences (ICAS2013) took place in Wuhan, P R China from 26-27 October 2013 at the Military Economics Academy. The conference is regularly organized, alternately in Romania and in P R China, by ''Politehnica'' University of Timişoara, Romania, and Military Economics Academy of Wuhan, P R China, with the aim to serve as a platform for the exchange of information between various areas of applied sciences, and to promote the communication between the scientists of different nations, countries and continents. The conference has been organized for the first time in 15-16 June 2012 at the Engineering Faculty of Hunedoara, Romania. The topics of the conference covered a comprehensive spectrum of issues: Economical sciences Engineering sciences Fundamental sciences Medical sciences The conference gathered qualified researchers whose expertise can be used to develop new engineering knowledge that has applicability potential in economics, defense, medicine, etc. The number of registered participants was nearly 90 from 5 countries. During the two days of the conference 4 invited and 36 oral talks were delivered. A few of the speakers deserve a special mention: Mircea Octavian Popoviciu, Academy of Romanian Scientist — Timişoara Branch, Correlations between mechanical properties and cavitation erosion resistance for stainless steels with 12% chromium and variable contents of nickel; Carmen Eleonora Hărău, ''Politehnica'' University of Timişoara, SWOT analysis of Romania's integration in EU; Ding Hui, Military Economics Academy of Wuhan, Design and engineering analysis of material procurement mobile operation platform; Serban Rosu, University of Medicine and Pharmacy ''Victor Babeş'' Timişoara, Cervical and facial infections — a real life threat, among others. Based on the work presented at the conference, 14 selected papers are included in this volume of IOP Conference Series: Materials Science and Engineering. These papers

International Conference on Applied Sciences (ICAS2013)

International Nuclear Information System (INIS)

Lemle, Ludovic Dan; Jiang, Yiwen

2014-01-01

The International Conference on Applied Sciences (ICAS2013) took place in Wuhan, P R China from 26–27 October 2013 at the Military Economics Academy. The conference is regularly organized, alternately in Romania and in P R China, by ''Politehnica'' University of Timişoara, Romania, and Military Economics Academy of Wuhan, P R China, with the aim to serve as a platform for the exchange of information between various areas of applied sciences, and to promote the communication between the scientists of different nations, countries and continents. The conference has been organized for the first time in 15–16 June 2012 at the Engineering Faculty of Hunedoara, Romania. The topics of the conference covered a comprehensive spectrum of issues: 1. Economical sciences; 2. Engineering sciences; 3. Fundamental sciences; 4. Medical sciences; The conference gathered qualified researchers whose expertise can be used to develop new engineering knowledge that has applicability potential in economics, defense, medicine, etc. The number of registered participants was nearly 90 from 5 countries. During the two days of the conference 4 invited and 36 oral talks were delivered. A few of the speakers deserve a special mention: Mircea Octavian Popoviciu, Academy of Romanian Scientist — Timişoara Branch, Correlations between mechanical properties and cavitation erosion resistance for stainless steels with 12% chromium and variable contents of nickel; Carmen Eleonora Hărău, ''Politehnica'' University of Timişoara, SWOT analysis of Romania's integration in EU; Ding Hui, Military Economics Academy of Wuhan, Design and engineering analysis of material procurement mobile operation platform; Serban Rosu, University of Medicine and Pharmacy ''Victor Babeş'' Timişoara, Cervical and facial infections — a real life threat, among others. Based on the work presented at the conference, 14 selected papers are included in this

Stimulation of ICa by basal PKA activity is facilitated by caveolin-3 in cardiac ventricular myocytes.

Science.gov (United States)

Bryant, Simon; Kimura, Tomomi E; Kong, Cherrie H T; Watson, Judy J; Chase, Anabelle; Suleiman, M Saadeh; James, Andrew F; Orchard, Clive H

2014-03-01

L-type Ca channels (LTCC), which play a key role in cardiac excitation-contraction coupling, are located predominantly at the transverse (t-) tubules in ventricular myocytes. Caveolae and the protein caveolin-3 (Cav-3) are also present at the t-tubules and have been implicated in localizing a number of signaling molecules, including protein kinase A (PKA) and β2-adrenoceptors. The present study investigated whether disruption of Cav-3 binding to its endogenous binding partners influenced LTCC activity. Ventricular myocytes were isolated from male Wistar rats and LTCC current (ICa) recorded using the whole-cell patch-clamp technique. Incubation of myocytes with a membrane-permeable peptide representing the scaffolding domain of Cav-3 (C3SD) reduced basal ICa amplitude in intact, but not detubulated, myocytes, and attenuated the stimulatory effects of the β2-adrenergic agonist zinterol on ICa. The PKA inhibitor H-89 also reduced basal ICa; however, the inhibitory effects of C3SD and H-89 on basal ICa amplitude were not summative. Under control conditions, myocytes stained with antibody against phosphorylated LTCC (pLTCC) displayed a striated pattern, presumably reflecting localization at the t-tubules. Both C3SD and H-89 reduced pLTCC staining at the z-lines but did not affect staining of total LTCC or Cav-3. These data are consistent with the idea that the effects of C3SD and H-89 share a common pathway, which involves PKA and is maximally inhibited by H-89, and suggest that Cav-3 plays an important role in mediating stimulation of ICa at the t-tubules via PKA-induced phosphorylation under basal conditions, and in response to β2-adrenoceptor stimulation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis.

Science.gov (United States)

Funane, Tsukasa; Atsumori, Hirokazu; Katura, Takusige; Obata, Akiko N; Sato, Hiroki; Tanikawa, Yukari; Okada, Eiji; Kiguchi, Masashi

2014-01-15

To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a "shared component" (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source-detector (S-D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S-D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S-D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation

Prefrontal cortex and somatosensory cortex in tactile crossmodal association: an independent component analysis of ERP recordings.

Directory of Open Access Journals (Sweden)

Yixuan Ku

2007-08-01

Full Text Available Our previous studies on scalp-recorded event-related potentials (ERPs showed that somatosensory N140 evoked by a tactile vibration in working memory tasks was enhanced when human subjects expected a coming visual stimulus that had been paired with the tactile stimulus. The results suggested that such enhancement represented the cortical activities involved in tactile-visual crossmodal association. In the present study, we further hypothesized that the enhancement represented the neural activities in somatosensory and frontal cortices in the crossmodal association. By applying independent component analysis (ICA to the ERP data, we found independent components (ICs located in the medial prefrontal cortex (around the anterior cingulate cortex, ACC and the primary somatosensory cortex (SI. The activity represented by the IC in SI cortex showed enhancement in expectation of the visual stimulus. Such differential activity thus suggested the participation of SI cortex in the task-related crossmodal association. Further, the coherence analysis and the Granger causality spectral analysis of the ICs showed that SI cortex appeared to cooperate with ACC in attention and perception of the tactile stimulus in crossmodal association. The results of our study support with new evidence an important idea in cortical neurophysiology: higher cognitive operations develop from the modality-specific sensory cortices (in the present study, SI cortex that are involved in sensation and perception of various stimuli.

Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

Science.gov (United States)

Thye, Melissa D; Ammons, Carla J; Murdaugh, Donna L; Kana, Rajesh K

2018-07-16

Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind (ToM). However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization. Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis (ICA), is a data-driven technique used to isolate intrinsic connectivity networks, and this approach provides insight into network-level regional recruitment. In this fMRI study, three complementary, but distinct ToM tasks assessing mental state detection (e.g. RMIE: Reading the Mind in the Eyes; RMIV: Reading the Mind in the Voice) and intentional attribution (Causality task) were each analyzed using ICA in order to separately characterize the recruitment and functional connectivity of core nodes in the ToM network in response to the sub-processes of ToM. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions. The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing. Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated

Empirical validation of the triple-code model of numerical processing for complex math operations using functional MRI and group Independent Component Analysis of the mental addition and subtraction of fractions.

Science.gov (United States)

Schmithorst, Vincent J; Brown, Rhonda Douglas

2004-07-01

The suitability of a previously hypothesized triple-code model of numerical processing, involving analog magnitude, auditory verbal, and visual Arabic codes of representation, was investigated for the complex mathematical task of the mental addition and subtraction of fractions. Functional magnetic resonance imaging (fMRI) data from 15 normal adult subjects were processed using exploratory group Independent Component Analysis (ICA). Separate task-related components were found with activation in bilateral inferior parietal, left perisylvian, and ventral occipitotemporal areas. These results support the hypothesized triple-code model corresponding to the activated regions found in the individual components and indicate that the triple-code model may be a suitable framework for analyzing the neuropsychological bases of the performance of complex mathematical tasks. Copyright 2004 Elsevier Inc.

New Evidences for Early Paracas Textiles and Ceramics at Cerrillos, Ica Valley, Perú

OpenAIRE

Splitstoser, Jeffrey; Wallace, Dwight D.; Delgado, Mercedes

2012-01-01

Cerrillos is an Early to Middle Paracas civic-ceremonial site located in the upper Ica Valley of Perú. The site is known for its finely plastered adobe architecture, beautifully decorated ceramics, and complex textiles, many of which are decorated with camelid hair. Cerrillos was located in a strategically important place where the mountains meet the coastal desert and the Ica River bends south, a likely intersection in a road system that connected Cerrillos to contemporary sites in the Parac...

Separation and reconstruction of high pressure water-jet reflective sound signal based on ICA

Science.gov (United States)

Yang, Hongtao; Sun, Yuling; Li, Meng; Zhang, Dongsu; Wu, Tianfeng

2011-12-01

The impact of high pressure water-jet on the different materials target will produce different reflective mixed sound. In order to reconstruct the reflective sound signals distribution on the linear detecting line accurately and to separate the environment noise effectively, the mixed sound signals acquired by linear mike array were processed by ICA. The basic principle of ICA and algorithm of FASTICA were described in detail. The emulation experiment was designed. The environment noise signal was simulated by using band-limited white noise and the reflective sound signal was simulated by using pulse signal. The reflective sound signal attenuation produced by the different distance transmission was simulated by weighting the sound signal with different contingencies. The mixed sound signals acquired by linear mike array were synthesized by using the above simulated signals and were whitened and separated by ICA. The final results verified that the environment noise separation and the reconstruction of the detecting-line sound distribution can be realized effectively.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

Science.gov (United States)

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens

2018-01-01

Background EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1

SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

Science.gov (United States)

Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

2017-09-01

With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.

On an efficient modification of singular value decomposition using independent component analysis for improved MRS denoising and quantification

International Nuclear Information System (INIS)

Stamatopoulos, V G; Karras, D A; Mertzios, B G

2009-01-01

An efficient modification of singular value decomposition (SVD) is proposed in this paper aiming at denoising and more importantly at quantifying more accurately the statistically independent spectra of metabolite sources in magnetic resonance spectroscopy (MRS). Although SVD is known in MRS applications and several efficient algorithms exist for estimating SVD summation terms in which the raw MRS data are analyzed, however, it would be more beneficial for such an analysis if techniques with the ability to estimate statistically independent spectra could be employed. SVD is known to separate signal and noise subspaces but it assumes orthogonal properties for the components comprising signal subspace, which is not always the case, and might impose heavy constraints for the MRS case. A much more relaxing constraint would be to assume statistically independent components. Therefore, a modification of the main methodology incorporating techniques for calculating the assumed statistically independent spectra is proposed by applying SVD on the MRS spectrogram through application of the short time Fourier transform (STFT). This approach is based on combining SVD on STFT spectrogram followed by an iterative application of independent component analysis (ICA). Moreover, it is shown that the proposed methodology combined with a regression analysis would lead to improved quantification of the MRS signals. An experimental study based on synthetic MRS signals has been conducted to evaluate the herein proposed methodologies. The results obtained have been discussed and it is shown to be quite promising

Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

Science.gov (United States)

Deepaisarn, S; Tar, P D; Thacker, N A; Seepujak, A; McMahon, A W

2018-01-01

Abstract Motivation Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. Results We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Availability and implementation Software is available via the open source image analysis system TINA Vision, www.tina-vision.net. Contact paul.tar@manchester.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29091994

Estudios sobre Trypanosomiasis americana en el Perú. Observaciones en el departamento de Ica

Directory of Open Access Journals (Sweden)

Víctor M Ayulo Robles

1946-01-01

Full Text Available Hemos comprobado la presencia de Triatomas en las ciudades de Ica y Nazca. Los Triatomas encontrados en esas localidades son de la especie del Triatoma Infestans. Las observaciones del contenido intestinal de los Triatomas capturados en ambas ciudades reveló que ellos no se encuentran infestados; lo que hace sospechar que estos insectos procedan de la Ciudad de Arequipa. Los exámenes de sangre en fresco, gota gruesa y frotis coloreados por el método de Giemsa en 174 personas y 32 animales, así como las pruebas de Desviación del Complemento (Reacción de GUERREIRO Y MACHADO e intradermo-reacción en las 174 personas, fueron negativas; lo que demuestra que actualmente no existe la Enfermedad de Chagas en el Departamento de Ica. Dada la presencia de Triatomas en Ica y Nazca y la existencia de la Enfermedad de Chagas en Vitor, Sihuas y Quishuarani del Departamento de Arequipa no se excluye la posibilidad de que posteriormente se haga presente la Trypanosomiasis americana, también, en esas zonas.

A network analysis of ¹⁵O-H₂O PET reveals deep brain stimulation effects on brain network of Parkinson's disease.

Science.gov (United States)

Park, Hae-Jeong; Park, Bumhee; Kim, Hae Yu; Oh, Maeng-Keun; Kim, Joong Il; Yoon, Misun; Lee, Jong Doo; Chang, Jin Woo

2015-05-01

As Parkinson's disease (PD) can be considered a network abnormality, the effects of deep brain stimulation (DBS) need to be investigated in the aspect of networks. This study aimed to examine how DBS of the bilateral subthalamic nucleus (STN) affects the motor networks of patients with idiopathic PD during motor performance and to show the feasibility of the network analysis using cross-sectional positron emission tomography (PET) images in DBS studies. We obtained [¹⁵O]H₂O PET images from ten patients with PD during a sequential finger-to-thumb opposition task and during the resting state, with DBS-On and DBS-Off at STN. To identify the alteration of motor networks in PD and their changes due to STN-DBS, we applied independent component analysis (ICA) to all the cross-sectional PET images. We analysed the strength of each component according to DBS effects, task effects and interaction effects. ICA blindly decomposed components of functionally associated distributed clusters, which were comparable to the results of univariate statistical parametric mapping. ICA further revealed that STN-DBS modifies usage-strengths of components corresponding to the basal ganglia-thalamo-cortical circuits in PD patients by increasing the hypoactive basal ganglia and by suppressing the hyperactive cortical motor areas, ventrolateral thalamus and cerebellum. Our results suggest that STN-DBS may affect not only the abnormal local activity, but also alter brain networks in patients with PD. This study also demonstrated the usefulness of ICA for cross-sectional PET data to reveal network modifications due to DBS, which was not observable using the subtraction method.

A Brief Background of the ICA (International Communication Association) Audit.

Science.gov (United States)

Krivonos, Paul D.

This paper examines the International Communication Association (ICA) audit, the aim of which is to establish an integrated communication audit system and a multimethod approach to the auditing of the communication of an organization. Many of an organization's communication variables and concepts are examined so that strengths and weaknesses in…

Blind Component Separation in Wavelet Space: Application to CMB Analysis

Directory of Open Access Journals (Sweden)

J. Delabrouille

2005-09-01

Full Text Available It is a recurrent issue in astronomical data analysis that observations are incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are nonstationary processes over the sky. All these effects impair data processing techniques which work in the Fourier domain. Spectral matching ICA (SMICA is a source separation method based on spectral matching in Fourier space designed for the separation of diffuse astrophysical emissions in cosmic microwave background observations. This paper proposes an extension of SMICA to the wavelet domain and demonstrates the effectiveness of wavelet-based statistics for dealing with gaps in the data.

Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering

International Nuclear Information System (INIS)

Oster, Julien; Pietquin, Olivier; Felblinger, Jacques; Abächerli, Roger; Kraemer, Michel

2009-01-01

Electrocardiogram (ECG) is required during magnetic resonance (MR) examination for monitoring patients under anaesthesia or with heart diseases and for synchronizing image acquisition with heart activity (triggering). Accurate and fast QRS detection is therefore desirable, but this task is complicated by artefacts related to the complex MR environment (high magnetic field, radio-frequency pulses and fast switching magnetic gradients). Specific signal processing has been proposed, whether using specific MR QRS detectors or ECG denoising methods. Most state-of-the-art techniques use a connection to the MR system for achieving their task, which is a major drawback since access to the MR system is often restricted. This paper introduces a new method for on-line ECG signal enhancement, called ICARE, which takes advantage of using multi-lead ECG and does not require any connection to the MR system. It is based on independent component analysis (ICA) and applied in real time. This algorithm yields accurate QRS detection for efficient triggering

Three-Dimensional Integrated Characterization and Archiving System (3D-ICAS). Phase 1

Energy Technology Data Exchange (ETDEWEB)

NONE

1994-07-01

3D-ICAS is being developed to support Decontamination and Decommissioning operations for DOE addressing Research Area 6 (characterization) of the Program Research and Development Announcement. 3D-ICAS provides in-situ 3-dimensional characterization of contaminated DOE facilities. Its multisensor probe contains a GC/MS (gas chromatography/mass spectrometry using noncontact infrared heating) sensor for organics, a molecular vibrational sensor for base material identification, and a radionuclide sensor for radioactive contaminants. It will provide real-time quantitative measurements of volatile organics and radionuclides on bare materials (concrete, asbestos, transite); it will provide 3-D display of the fusion of all measurements; and it will archive the measurements for regulatory documentation. It consists of two robotic mobile platforms that operate in hazardous environments linked to an integrated workstation in a safe environment.

Three-Dimensional Integrated Characterization and Archiving System (3D-ICAS). Phase 1

International Nuclear Information System (INIS)

1994-07-01

3D-ICAS is being developed to support Decontamination and Decommissioning operations for DOE addressing Research Area 6 (characterization) of the Program Research and Development Announcement. 3D-ICAS provides in-situ 3-dimensional characterization of contaminated DOE facilities. Its multisensor probe contains a GC/MS (gas chromatography/mass spectrometry using noncontact infrared heating) sensor for organics, a molecular vibrational sensor for base material identification, and a radionuclide sensor for radioactive contaminants. It will provide real-time quantitative measurements of volatile organics and radionuclides on bare materials (concrete, asbestos, transite); it will provide 3-D display of the fusion of all measurements; and it will archive the measurements for regulatory documentation. It consists of two robotic mobile platforms that operate in hazardous environments linked to an integrated workstation in a safe environment

Avaliação da qualidade protéica de dois suplementos alimentares em ratos Wistar

Directory of Open Access Journals (Sweden)

M. M. V. NAVES

2008-10-01

Full Text Available

O objetivo da pesquisa foi avaliar a qualidade protéica de dois suplementos alimentares - um, formulado protéico em pó (produto em fase de teste e gelatina. Seis grupos de seis ratos machos Wistar recém-desmamados foram alimentados durante catorze dias com as seguintes dietas: controle (C10 - caseína, formulado protéico (FP10 e gelatina (G10 ao nível de 10 % de proteína; misturas de caseíma com formulado protéico (C7 FP3 ou gelatina (C7G3 (7:3 em base protéica, respectivamente; e dieta aprotéica. As dietas contendo o formulado protéico apresentaram boa qualidade protéica, segundo os índices NPR (Net Protein Ratio e NPU (Net Protein Utilization, sem diferenças significativas entre C7 FP3 e C10 (NPR= 4,00 e 4,11, e NPU= 65% e 67%, respectivamente. Os animais alimentados com gelatina (G10 e com dieta aprotéica perderam peso corpóreo e apresentaram pesos semelhantes dos fígados e inferiores aos dos demais grupos de ratos. O valor protéico da mistura caseína-gelatina (C7G3 foi menor que o da caseína (NPR relativo= 77 %, p<0,05. O formulado protéico constitui uma fonte de proteína de boa qualidade e seu uso como suplemento alimentar pode ser indicado em situações especiais. A gelatina, por sua vez, não pode ser considerada um complemento ou suplemento nutricional, pois não atende às necessidades protéicas do organismo e pode prejudicar a disponibilidade biológica de proteínas de boa qualidade.

: Toward a conceptualization of a paracas urbanism in ánimas altas / ánimas bajas (Ica Valley)?

OpenAIRE

Bachir Bacha , Aïcha; Llanos Jacinto , Oscar Daniel

2015-01-01

International audience; Since 2009, within the framework of the Ánimas Altas Archaeological Program in Ica, Peru — under the direction of the au-thors — systematic excavations have been carried out in the Ánimas Altas/Ánimas Bajas archaeological complex, the center ofthe Paracas culture in the lower Ica valley. To date, the project has documented public-ceremonial architecture as well as domesticand production areas. Additionally, excavations have revealed evidence of pyramidal complexes cont...

Innovative high-performance liquid chromatography method development for the screening of 19 antimalarial drugs based on a generic approach, using design of experiments, independent component analysis and design space.

Science.gov (United States)

Debrus, B; Lebrun, P; Kindenge, J Mbinze; Lecomte, F; Ceccato, A; Caliaro, G; Mbay, J Mavar Tayey; Boulanger, B; Marini, R D; Rozet, E; Hubert, Ph

2011-08-05

An innovative methodology based on design of experiments (DoE), independent component analysis (ICA) and design space (DS) was developed in previous works and was tested out with a mixture of 19 antimalarial drugs. This global LC method development methodology (i.e. DoE-ICA-DS) was used to optimize the separation of 19 antimalarial drugs to obtain a screening method. DoE-ICA-DS methodology is fully compliant with the current trend of quality by design. DoE was used to define the set of experiments to model the retention times at the beginning, the apex and the end of each peak. Furthermore, ICA was used to numerically separate coeluting peaks and estimate their unbiased retention times. Gradient time, temperature and pH were selected as the factors of a full factorial design. These retention times were modelled by stepwise multiple linear regressions. A recently introduced critical quality attribute, namely the separation criterion (S), was also used to assess the quality of separations rather than using the resolution. Furthermore, the resulting mathematical models were also studied from a chromatographic point of view to understand and investigate the chromatographic behaviour of each compound. Good adequacies were found between the mathematical models and the expected chromatographic behaviours predicted by chromatographic theory. Finally, focusing at quality risk management, the DS was computed as the multidimensional subspace where the probability for the separation criterion to lie in acceptance limits was higher than a defined quality level. The DS was computed propagating the prediction error from the modelled responses to the quality criterion using Monte Carlo simulations. DoE-ICA-DS allowed encountering optimal operating conditions to obtain a robust screening method for the 19 considered antimalarial drugs in the framework of the fight against counterfeit medicines. Moreover and only on the basis of the same data set, a dedicated method for the

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

Science.gov (United States)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

DEFF Research Database (Denmark)

Dyrholm, Mads; Hansen, Lars Kai

2004-01-01

We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....

Qualidade protéica do soro de leite fermentado pela levedura Kluyveromyces fragilis

OpenAIRE

Silva,Caio Abércio da; Hernan-Gomez,Raul Castro

2000-01-01

O soro de leite fermentado pela levedura Kluyveromyces fragilis, após secagem em spray drier, foi submetido à avaliação da qualidade protéica através de uma análise aminoacídica e de um estudo biológico pelos métodos: Relação da Eficiência Protéica (PER), Relação da Eficiência Líquida da Proteína (NPR) e Utilização Líquida da Proteína (NPU). Na análise aminoacídica, foram utilizadas como comparativo a composição de aminoácidos das proteínas padrão do ovo e da FAO. Os resultados indicaram um a...

Increasing ICA512 autoantibody titers predict development of abnormal oral glucose tolerance tests.

Science.gov (United States)

Sanda, Srinath

2018-03-01

Determine if autoantibody titer magnitude and variability predict glucose abnormalities in subjects at risk for type 1 diabetes. Demographic information, longitudinal autoantibody titers, and oral glucose tolerance test (OGTT) data were obtained from the TrialNet Pathway to Prevention study. Subjects (first and second degree relatives of individuals with type 1 diabetes) with at least 2 diabetes autoantibodies were selected for analysis. Autoantibody titer means were calculated for each subject for the duration of study participation and the relationship between titer tertiles and glucose value tertiles from OGTTs (normal, impaired, and diabetes) was assessed with a proportional odds ordinal regression model. A matched pairs analysis was used to examine the relationship between changes in individual autoantibody titers and 120-minute glucose values. Titer variability was quantified using cumulative titer standard deviations. We studied 778 subjects recruited in the TrialNet Pathway to Prevention study between 2006 and 2014. Increased cumulative mean titer values for both ICA512 and GAD65 (estimated increase in proportional odds = 1.61, 95% CI = 1.39, 1.87, P < 1 × 10 -9 and 1.17, 95% CI = 1.03, 1.32, P = .016, respectively) were associated with peak 120-minute glucose values. While fluctuating titer levels were observed in some subjects, no significant relationship between titer standard deviation and glucose values was observed. ICA512 autoantibody titers associate with progressive abnormalities in glucose metabolism in subjects at risk for type 1 diabetes. Fluctuations in autoantibody titers do not correlate with lower rates of progression to clinical disease. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis.

Science.gov (United States)

Lin, Yuan-Pin; Duann, Jeng-Ren; Feng, Wenfeng; Chen, Jyh-Horng; Jung, Tzyy-Ping

2014-02-28

Music conveys emotion by manipulating musical structures, particularly musical mode- and tempo-impact. The neural correlates of musical mode and tempo perception revealed by electroencephalography (EEG) have not been adequately addressed in the literature. This study used independent component analysis (ICA) to systematically assess spatio-spectral EEG dynamics associated with the changes of musical mode and tempo. Empirical results showed that music with major mode augmented delta-band activity over the right sensorimotor cortex, suppressed theta activity over the superior parietal cortex, and moderately suppressed beta activity over the medial frontal cortex, compared to minor-mode music, whereas fast-tempo music engaged significant alpha suppression over the right sensorimotor cortex. The resultant EEG brain sources were comparable with previous studies obtained by other neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). In conjunction with advanced dry and mobile EEG technology, the EEG results might facilitate the translation from laboratory-oriented research to real-life applications for music therapy, training and entertainment in naturalistic environments.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively.

Science.gov (United States)

Shimamoto, Shoichi; Waldman, Zachary J; Orosz, Iren; Song, Inkyung; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard; Sharan, Ashwini; Wu, Chengyuan; Sperling, Michael R; Weiss, Shennan A

2018-01-01

To develop and validate a detector that identifies ripple (80-200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). iEEG recordings from 16 patients were first band-pass filtered (80-600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of

Towards Cognitive Component Analysis

DEFF Research Database (Denmark)

Hansen, Lars Kai; Ahrendt, Peter; Larsen, Jan

2005-01-01

Cognitive component analysis (COCA) is here defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. We have earlier demonstrated that independent components analysis is relevant for representing...

Suppression of the µ rhythm during speech and non-speech discrimination revealed by independent component analysis: implications for sensorimotor integration in speech processing.

Science.gov (United States)

Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan

2013-01-01

Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.). Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80-100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDRspeech discrimination trials relative to chance trials following stimulus offset. Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.

Neural Correlates Associated with Successful Working Memory Performance in Older Adults as Revealed by Spatial ICA

Science.gov (United States)

Saliasi, Emi; Geerligs, Linda; Lorist, Monicque M.; Maurits, Natasha M.

2014-01-01

To investigate which neural correlates are associated with successful working memory performance, fMRI was recorded in healthy younger and older adults during performance on an n-back task with varying task demands. To identify functional networks supporting working memory processes, we used independent component analysis (ICA) decomposition of the fMRI data. Compared to younger adults, older adults showed a larger neural (BOLD) response in the more complex (2-back) than in the baseline (0-back) task condition, in the ventral lateral prefrontal cortex (VLPFC) and in the right fronto-parietal network (FPN). Our results indicated that a higher BOLD response in the VLPFC was associated with increased performance accuracy in older adults, in both the baseline and the more complex task condition. This ‘BOLD-performance’ relationship suggests that the neural correlates linked with successful performance in the older adults are not uniquely related to specific working memory processes present in the complex but not in the baseline task condition. Furthermore, the selective presence of this relationship in older but not in younger adults suggests that increased neural activity in the VLPFC serves a compensatory role in the aging brain which benefits task performance in the elderly. PMID:24911016

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

OpenAIRE

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic...

ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison.

NARCIS (Netherlands)

Zwanenburg, G.; Hoefsloot, H.C.J.; Westerhuis, J.A.; Jansen, J.J.; Smilde, A.K.

2011-01-01

ANOVA-simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements.

Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

Science.gov (United States)

Berradja, Khadidja; Boughanmi, Nabil

2016-09-01

In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p corrupted with spillover.

DISEÑO DE PÉPTIDOS BASADO EN LA SECUENCIA ANÁLOGA AL REPRESOR NEGATIVO icaR DE Staphylococcus sp.

Directory of Open Access Journals (Sweden)

Gladys Pinilla

2015-05-01

Full Text Available La biopelícula como un mecanismo de virulencia en Staphylococcus involucrada en infecciones intrahospitalarias es regulada por un represor negativo icaR, responsable de la transcripción completa del operón icaADBC. La  búsqueda de dominios funcionales por modulación computacional de icaR permitió  hallar las secuencias peptídicas con actividad biológica análoga a la proteína icaR. Mediante biología computacional se diseñaron péptidos empleando el programa de predicción AntiBP (http: //www. imtech.res.in/ raghava/antibp/; la síntesis química se hizo por Nα-Fmoc y se   caracterizaron y purificaron  tres moléculas por RP-HPLC y MALDI-TOF. Se evaluó su seguridad biológica mediante ensayo de actividad citotóxica  realizada sobre macrófagos murinos de la línea J774 y la actividad hemolítica se determinó mediante el uso de glóbulos rojos. Los tres péptidos caracterizados IR1, IR2 e IR3, presentaron estructura secundaria       predominantemente alfa helicoidal, alto grado de pureza y alto score antimicrobiano; además,  mostraron baja toxicidad, evidenciada por la actividad citotóxica y hemolítica en las concentraciones ensayadas y en comparación con los controles usados, que permitiría su potencial uso como moléculas candidatas o principios activos con actividad análoga al represor nativo icaR, frente a la biopelícula de los Staphylococcus sp.

Cognitive Component Analysis

DEFF Research Database (Denmark)

Feng, Ling

2008-01-01

This dissertation concerns the investigation of the consistency of statistical regularities in a signaling ecology and human cognition, while inferring appropriate actions for a speech-based perceptual task. It is based on unsupervised Independent Component Analysis providing a rich spectrum...... of audio contexts along with pattern recognition methods to map components to known contexts. It also involves looking for the right representations for auditory inputs, i.e. the data analytic processing pipelines invoked by human brains. The main ideas refer to Cognitive Component Analysis, defined...... as the process of unsupervised grouping of generic data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. Its hypothesis runs ecologically: features which are essentially independent in a context defined ensemble, can be efficiently coded as sparse...

Gaussian process based independent analysis for temporal source separation in fMRI

DEFF Research Database (Denmark)

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-01-01

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts...... the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its...

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

Science.gov (United States)

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

Independent component analysis: recent advances

OpenAIRE

Hyv?rinen, Aapo

2013-01-01

Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in th...

Optimal model of PDIG based microgrid and design of complementary stabilizer using ICA.

Science.gov (United States)

Amini, R Mohammad; Safari, A; Ravadanegh, S Najafi

2016-09-01

The generalized Heffron-Phillips model (GHPM) for a microgrid containing a photovoltaic (PV)-diesel machine (DM)-induction motor (IM)-governor (GV) (PDIG) has been developed at the low voltage level. A GHPM is calculated by linearization method about a loading condition. An effective Maximum Power Point Tracking (MPPT) approach for PV network has been done using sliding mode control (SMC) to maximize output power. Additionally, to improve stability of microgrid for more penetration of renewable energy resources with nonlinear load, a complementary stabilizer has been presented. Imperialist competitive algorithm (ICA) is utilized to design of gains for the complementary stabilizer with the multiobjective function. The stability analysis of the PDIG system has been completed with eigenvalues analysis and nonlinear simulations. Robustness and validity of the proposed controllers on damping of electromechanical modes examine through time domain simulation under input mechanical torque disturbances. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Principal component regression analysis with SPSS.

Science.gov (United States)

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

2003-06-01

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

Barreras en el desarrollo de la agroindustria del espárrago en la Región Ica

OpenAIRE

Carpio Ventura, Jesús Eliberto; Gaona Abad, Linda Sue; Uculmana Villagómez, Luisa Margarita; Vilca Antezana, Shaska Helena Isabel

2015-01-01

ix, 135 h. : il. ; 30 cm. En los últimos años, el espárrago ha tenido una importante y creciente participación en la actividad económica para la región Ica, representando al 2013 el 39.85% de la producción nacional (Series Históricas de Producción Agrícola – Compendio Estadístico, 2013). Asimismo, desde el 2011 hasta el 2013 Ica ha sido la primera región agroexportadora de espárrago, contribuyendo en el 2013 con el 42.68% del total de toneladas comercializadas a nivel nacional, según el Si...

Plant remains of archaeological site Casa Vieja, Callango (Ica

Directory of Open Access Journals (Sweden)

José Roque

2013-06-01

Full Text Available A paleoethnobotanical study was carried out at the Middle Horizon archaeological site of Casa Vieja, located in Callango within the Lower Ica Valley. A total of 23 species were identified, all determined to be of the Magnoliopyta Division, 78 % (or 18 species were Magnoliopsid and 22% (or 15 species Liliopsid. The Fabaceae are the best represented family with 6 species. Most of the analyzed samples correspond to seeds of Gossypium barbadense “cotton”. Seventy percent of the species were probably used as food; 48% for artifact-making and construction and 52% for medicinal and curative purposes.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

Science.gov (United States)

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Multiscale principal component analysis

International Nuclear Information System (INIS)

Akinduko, A A; Gorban, A N

2014-01-01

Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between data projections. This definition opens up more flexibility in the analysis of principal components which is useful in enhancing PCA. In this paper we introduce scales into PCA by maximizing only the sum of pairwise distances between projections for pairs of datapoints with distances within a chosen interval of values [l,u]. The resulting principal component decompositions in Multiscale PCA depend on point (l,u) on the plane and for each point we define projectors onto principal components. Cluster analysis of these projectors reveals the structures in the data at various scales. Each structure is described by the eigenvectors at the medoid point of the cluster which represent the structure. We also use the distortion of projections as a criterion for choosing an appropriate scale especially for data with outliers. This method was tested on both artificial distribution of data and real data. For data with multiscale structures, the method was able to reveal the different structures of the data and also to reduce the effect of outliers in the principal component analysis

Independent EEG sources are dipolar.

Directory of Open Access Journals (Sweden)

Arnaud Delorme

Full Text Available Independent component analysis (ICA and blind source separation (BSS methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA; best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison.

Attenuation of artifacts in EEG signals measured inside an MRI scanner using constrained independent component analysis

International Nuclear Information System (INIS)

Rasheed, Tahir; Lee, Young-Koo; Lee, Soo Yeol; Kim, Tae-Seong

2009-01-01

Integration of electroencephalography (EEG) and functional magnetic imaging (fMRI) resonance will allow analysis of the brain activities at superior temporal and spatial resolution. However simultaneous acquisition of EEG and fMRI is hindered by the enhancement of artifacts in EEG, the most prominent of which are ballistocardiogram (BCG) and electro-oculogram (EOG) artifacts. The situation gets even worse if the evoked potentials are measured inside MRI for their minute responses in comparison to the spontaneous brain responses. In this study, we propose a new method of attenuating these artifacts from the spontaneous and evoked EEG data acquired inside an MRI scanner using constrained independent component analysis with a priori information about the artifacts as constraints. With the proposed techniques of reference function generation for the BCG and EOG artifacts as constraints, our new approach performs significantly better than the averaged artifact subtraction (AAS) method. The proposed method could be an alternative to the conventional ICA method for artifact attenuation, with some advantages. As a performance measure we have achieved much improved normalized power spectrum ratios (INPS) for continuous EEG and correlation coefficient (cc) values with outside MRI visual evoked potentials for visual evoked EEG, as compared to those obtained with the AAS method. The results show that our new approach is more effective than the conventional methods, almost fully automatic, and no extra ECG signal measurements are involved

Sledovi pleistocenske poledenitve Stare Galičice, Makedonija = Remmants of Pleistocene glaciation on Stara Galičica Mountain, Macedonia

Directory of Open Access Journals (Sweden)

Uroš Stepišnik

2012-01-01

Full Text Available The Stara Galičica Mountain was highly reshaped by glacial processes during the Pleistocene.Based on the detailed morphographical mapping of glacial features, the extent ofglaciation was determined as being far larger than described in previous references. The innerpart of the massif was covered with an ice-cap from where outlet glaciers were flowingout. On the basis of morphometrical analysis of glacial features, the altitude of the glacierequilibrium line during the Last Glacial Maximum was established at 1840 m a.s.l.

Lessons learnt from 15 years of ICA in anaerobic digesters

DEFF Research Database (Denmark)

Steyer, J.P.; Bernard, O.; Batstone, Damien J.

2006-01-01

for application of instrumentation, control and automation (ICA) in the field of anaerobic digestion. This paper will discuss the requirements (in terms of on-line sensors needed, modelling efforts and mathematical complexity) but also the advantages and drawbacks of different control strategies that have been......Anaerobic digestion plants are highly efficient wastewater treatment processes with inherent energy production. Despite these advantages, many industries are still reluctant to use them because of their instability confronted with changes in operating conditions. There is therefore great potential...... applied to AD high rate processes over the last 15 years....

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

Science.gov (United States)

Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

2018-01-01

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Generalized structured component analysis a component-based approach to structural equation modeling

CERN Document Server

Hwang, Heungsun

2014-01-01

Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new a...

MEXnICA, Mexican group in the MPD-NICA experiment at JINR

Science.gov (United States)

Rodríguez Cahuantzi, M.; MEXnICA Group

2017-10-01

The Nuclotron Ion Collider fAcility (NICA) accelerator complex is currently under construction at the Joint Institute for Nuclear Research (JINR) laboratory located in the city of Dubna in the Russian Federation. The main goal of NICA is to collide heavy ion nuclei to study the properties of the phase diagram of strongly interacting matter at high baryon density. In this accelerator complex, two big particle detectors are planned to be installed: Spin Physics Detector (SPD) and Multi-Purpose Detector (MPD). At the design luminosity, the event rate in the MPD interaction region is about 6 kHz; the total charged particle multiplicity would exceeds 1000 in the most central Au+Au collisions at \\sqrt{{sNN}} = 11 {{GeV}}. Since the middle of 2016 a group of researchers and students from Mexican institutions was formed (MEXnICA). The main goal of the MEXnICA group is to collaborate in the experimental efforts of MPD-NICA proposing a BEam-BEam counter detector which we called BEBE. In this written general aspects of MPD-NICA detector and BEBE are discussed. This material was shown in a contributed talk given at the XXXI Annual Meeting of the Mexican Division of Particles and Fields held in the Physics Department of CINVESTAV located in Mexico City during the last week of May 2017.

Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

DEFF Research Database (Denmark)

Bang-Berthelsen, Claus Heiner; Pedersen, Lykke; Fløyel, Tina

2011-01-01

enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY). CONCLUSIONS: In this study, ICA...... (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes. RESULTS: Microrray profiling identified eight miRNAs (miR-124...... between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes....

Efeitos da desnutrição protéica pós-natal no desempenho de ratos em diferentes tarefas de aprendizagem e memória

OpenAIRE

Camila Tavares Valadares

2006-01-01

A desnutrição protéica em estágios iniciais da vida produz alterações estruturais, neuroquímicas e funcionais no sistema nervoso central, podendo causar prejuízos no desenvolvimento cognitivo e comportamental. Assim, o objetivo do presente estudo foi o de investigar os efeitos da desnutrição protéica pós-natal em tarefas que avaliam a aprendizagem e memória. Estas investigações foram feitas comparando o desempenho de ratos Wistar, submetidos à desnutrição protéica (6% de proteína) durante a l...

Analyse préliminaire de la situation et des perspectives de la culture du haricot de Lima (Phaseolus lunatus L. sur la Côte péruvienne (Vallées d'Ica, Pisco et Casma

Directory of Open Access Journals (Sweden)

Baudoin J.P.

1999-01-01

Full Text Available Preliminary analysis of the situation and prospects of the Lima bean crop (Phaseolus lunatus L. in the Peruvian Coast (Valleys of Ica, Pisco and Casma. The Lima bean, Phaseolus lunatus L., is a crop of regional importance on the Peruvian Coast. Within the framework of a collaborative project between the ""faculté universitaire des Sciences agronomiques'"" in Gembloux and the ""Universidad Nacional Agraria La Molina"" in Lima, we carried out a diagnosis of this speculation in the Ica, Pisco and Casma valleys in order to define the constraints which limit crop yields and to suggest improvements within the reach of the smallholders. To achieve these objectives we carried out a formal survey, centred on the Lima bean crop and smallholder relations with the agro-socio-economical environment, and an informal survey, centred on the studied farm systems. To complete these data we met some key informants belonging to all the sectors in contact with agriculture. This study allowed us to identify five undersystems in the farm systems of the Peruvian Coastal Valleys. These undersystems are: cotton, commercial food crops, self-subsistence food crops, livestock and fruit trees. The Lima bean usually belongs to the commercial food crops undersystem. There are two types of constraints. External constraints affect all the components of the farm system and are mainly: end of State support to agriculture, liberalization of trade and unavailability of credit. Internal constraints directly affect the Lima bean crop. Low income leads to a deficiency in pest control and adequate crop management. The Lima bean is also in competition with other components of the system such as cotton and common beans

Presencia del Paijanense en el desierto de Ica

Directory of Open Access Journals (Sweden)

1990-01-01

Full Text Available PRESENCE DU PAIJANIEN DANS LE DESERT DE ICA. Dans plusieurs publications, Frédéric Engel donne des informations qui suggèrent la présence de Paijanien dans la zone de la péninsule de Paracas. Après une minutieuse analyse des publications, des collections d’Engel et après des prospections de terrain menées en dépit des imprécisions existantes dans ses écrits, les auteurs ont pu trouver des évidences de restes qui du point de vue technologique, correspondent au Paijanien. Ils décrivent donc leurs trouvailles et analysent de manière critique les matériaux des collections d’Engel. En algunas publicaciones Frédéric Engel da informaciones que sugieren la presencia de la cultura Paijanense en el área de la Península de Paracas. A pesar de las imprecisiones existentes en los escritos de este autor, después de un prolijo análisis documental de las colecciones hechas por éste, además de trabajos de campo, los autores han podido encontrar las evidencias de restos que, tecnológicamente, corresponden a la industria Paijanense. Se describe el hallazgo y, al mismo tiempo, se discute críticamente los materiales existentes en las colecciones de Engel. PAIJÁN PRESENCE IN THE DESERT OF ICA. Several publications of Frédéric Engel have presented information that suggests the presence of the Paiján culture in the Paracas peninsula area. In spite of the inherent lack of precision in Engel’s writings, after an extensive review of the documentation and collections made by Engel and their survey of the area, the authors found evidence of remains that technologically correspond to the Paiján culture. They describe their finds and critically examine the materials in Engel’s collections.

Single channel blind source separation based on ICA feature extraction

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

A new technique is proposed to solve the blind source separation (BSS) given only a single channel observation. The basis functions and the density of the coefficients of source signals learned by ICA are used as the prior knowledge. Based on the learned prior information the learning rules of single channel BSS are presented by maximizing the joint log likelihood of the mixed sources to obtain source signals from single observation,in which the posterior density of the given measurements is maximized. The experimental results exhibit a successful separation performance for mixtures of speech and music signals.

OCCURRENCE OF LEAD-ZINK ORE AT Mt. IVANŠČICA NEAR IVANEC (CROATIA

Directory of Open Access Journals (Sweden)

Boris Šinkovec

2000-12-01

Full Text Available Mineral and ore occurences at Mt. Ivanščica are situated in the Middle Triassic carbonate rocks and are of epigenetic origin. These occurences are characterised by simple paragenesis of primary sulphides of leads and zinc and traces of sulphides of iron and copper. This mineralization is similar to Mississippi Valley Pb-Zn deposits (the paper is published in Croatian.

Multiview Bayesian Correlated Component Analysis

DEFF Research Database (Denmark)

Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai

2015-01-01

are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....

Two-Microphone Separation of Speech Mixtures

DEFF Research Database (Denmark)

Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan

2008-01-01

combined, independent component analysis (ICA) and binary time–frequency (T–F) masking. By estimating binary masks from the outputs of an ICA algorithm, it is possible in an iterative way to extract basis speech signals from a convolutive mixture. The basis signals are afterwards improved by grouping...

Analysis of independent components of cognitive event related potentials in a group of ADHD adults.

Science.gov (United States)

Markovska-Simoska, Silvana; Pop-Jordanova, Nada; Pop-Jordanov, Jordan

In the last decade, many studies have tried to define the neural correlates of attention deficit hyperactivity disorder (ADHD). The main aim of this study is the comparison of the ERPs independent components in the four QEEG subtypes in a group of ADHD adults as a basis for defining the corresponding endophenotypes among ADHD population. Sixty-seven adults diagnosed as ADHD according to the DSM-IV criteria and 50 age-matched control subjects participated in the study. The brain activity of the subjects was recorded by 19 channel quantitative electroencephalography (QEEG) system in two neuropsychological tasks (visual and emotional continuous performance tests). The ICA method was applied for separation of the independent ERPs components. The components were associated with distinct psychological operations, such as engagement operations (P3bP component), comparison (vcomTL and vcom TR), motor inhibition (P3supF) and monitoring (P4monCC) operations. The ERPs results point out that there is disturbance in executive functioning in investigated ADHD group obtained by the significantly lower amplitude and longer latency for the engagement (P3bP), motor inhibition (P3supF) and monitoring (P4monCC) components. Particularly, the QEEG subtype IV was with the most significant ERPs differences comparing to the other subtypes. In particular, the most prominent difference in the ERPs independent components for the QEEG subtype IV in comparison to other three subtypes, rise many questions and becomes the subject for future research. This study aims to advance and facilitate the use of neurophysiological procedures (QEEG and ERPs) in clinical practice as objective measures of ADHD for better assessment, subtyping and treatment of ADHD.

The effect of automatic blink correction on auditory evoked potentials.

Science.gov (United States)

Korpela, Jussi; Vigário, Ricardo; Huotilainen, Minna

2012-01-01

The effects of blink correction on auditory event-related potential (ERP) waveforms is assessed. Two blink correction strategies are compared. ICA-SSP combines independent component analysis (ICA) with signal space projection (SSP) and ICA-EMD uses empirical mode decomposition (EMD) to improve the performance of the standard ICA method. Five voluntary subjects performed an auditory oddball task. The resulting ERPs are used to compare the two blink correction methods to each other and against blink rejection. The results suggest that both methods qualitatively preserve the ERP waveform but that they underestimate some of the peak amplitudes. ICA-EMD performs slightly better than ICA-SSP. In conclusion, the use of blink correction is justified, especially if blink rejection leads to severe data loss.

Transferring the Incremental Capacity Analysis to Lithium-Sulfur Batteries

DEFF Research Database (Denmark)

Knap, Vaclav; Kalogiannis, Theodoros; Purkayastha, Rajlakshmi

2017-01-01

In order to investigate the battery degradation and to estimate their health, various techniques can be applied. One of them, which is widely used for Lithium-ion batteries, is the incremental capacity analysis (ICA). In this work, we apply the ICA to Lithium-Sulfur batteries, which differ in many...... aspects from Lithium-ion batteries and possess unique behavior. One of the challenges of applying the ICA to Lithium-Sulfur batteries is the representation of the IC curves, as their voltage profiles are often non-monotonic, resulting in more complex IC curves. The ICA is at first applied to charge...

Model reduction by weighted Component Cost Analysis

Science.gov (United States)

Kim, Jae H.; Skelton, Robert E.

1990-01-01

Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called 'component cost' to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. A reduced-order model of the given system may be obtained by deleting those components that have the smallest component costs. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. Closed-form analytical expressions of component costs are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems. A numerical example for MINIMAST system is presented.

Evaluation of I/Ca ratios in benthic foraminifera from the Peruvian oxygen minimum zone as proxy for redox conditions in the ambient water masses

Science.gov (United States)

Glock, N.; Liebetrau, V.; Eisenhauer, A.

2014-12-01

Tropical oxygen minimum zones (OMZs) are most important areas of oxygen depletion in today´s oceans and nutrient cycling in these regions has a large socio-economic impact because they account for about 17% of the global commercial fish catches(1). Possibly increasing magnitude and area of oxygen depletion in these regions, might endanger rich pelagic fish habitats in the future threatening the global marine food supply. By the use of a quantitative redox proxy in OMZs, reconstruction of the temporal variation in OMZ extension eventually providing information about past and future changes in oxygenation and the anthropogenic role in the recent trend of expanding OMZs(2). Recent work has shown that iodine/calcium (I/Ca) ratios in marine carbonates are a promising proxy for ambient oxygen concentration(3). Our study explores the correlation of I/Ca ratios in four benthic foraminiferal species (three calcitic, one aragonitic) from the Peruvian OMZ to bottom water oxygen concentrations ([O2]BW) and evaluates foraminiferal I/Ca ratios as a possible redox proxy for the ambient water masses. Our results show that all species have a positive trend in the I/Ca ratios as a function of [O2]BW. Only for the aragonitic species Hoeglundina elegans this trend is not significant. The highest significance has been found for Uvigerina striata (I/Ca = 0.032(±0.004).[O2]BW + 0.29(±0.03), R² = 0.61, F = 75, P solutions, (ii) a species dependency of the I/Ca-[O2]BW relationship which is either related to a strong vital effect or toa species dependency on the calcification depth within sediment, and (iii) the inter-test variability of I/Ca between different specimens from the same species and habitat. (1): FAO FishStat: Fisheries and aquaculture software. In: FAO Fisheries and Aquaculture Department[online]. Rome. Updated 28 Nov. 2013. (2): Stramma et al.: Expanding Oxygen-Minimum Zones in the Tropical Oceans, Science, 320, 655-658, 2008. (3): Lu et al.: Iodine to calcium ratios in

ComDim for explorative multi-block data analysis of Cantal-type cheeses: Effects of salts, gentle heating and ripening.

Science.gov (United States)

Loudiyi, M; Rutledge, D N; Aït-Kaddour, A

2018-10-30

Common Dimension (ComDim) chemometrics method for multi-block data analysis was employed to evaluate the impact of different added salts and ripening times on physicochemical, color, dynamic low amplitude oscillatory rheology, texture profile, and molecular structure (fluorescence and MIR spectroscopies) of five Cantal-type cheeses. Firstly, Independent Components Analysis (ICA) was applied separately on fluorescence and MIR spectra in order to extract the relevant signal source and the associated proportions related to molecular structure characteristics. ComDim was then applied on the 31 data tables corresponding to the proportion of ICA signals obtained for spectral methods and the global analysis of cheeses by the other techniques. The ComDim results indicated that generally cheeses made with 50% NaCl or with 75:25% NaCl/KCl exhibit the equivalent characteristics in structural, textural, meltability and color properties. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different techniques describe the same samples. Copyright © 2018 Elsevier Ltd. All rights reserved.

Signal recovery in imaging photoplethysmography

International Nuclear Information System (INIS)

Holton, Benjamin D; Mannapperuma, Kavan; Lesniewski, Peter J; Thomas, John C

2013-01-01

Imaging photoplethysmography is an emerging technique for the extraction of biometric information from people using video recordings. The focus is on extracting the cardiac heart rate of the subject by analysing the luminance of the colour video signal and identifying periodic components. Advanced signal processing is needed to recover the information required. In this paper, independent component analysis (ICA), principal component analysis, auto- and cross-correlation are investigated and compared with respect to their effectiveness in extracting the relevant information from video recordings. Results obtained are compared with those recorded by a modern commercial finger pulse oximeter. It is found that ICA produces the most consistent results. (paper)

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

Science.gov (United States)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

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

Signal recovery in imaging photoplethysmography.

Science.gov (United States)

Holton, Benjamin D; Mannapperuma, Kavan; Lesniewski, Peter J; Thomas, John C

2013-11-01

Imaging photoplethysmography is an emerging technique for the extraction of biometric information from people using video recordings. The focus is on extracting the cardiac heart rate of the subject by analysing the luminance of the colour video signal and identifying periodic components. Advanced signal processing is needed to recover the information required. In this paper, independent component analysis (ICA), principal component analysis, auto- and cross-correlation are investigated and compared with respect to their effectiveness in extracting the relevant information from video recordings. Results obtained are compared with those recorded by a modern commercial finger pulse oximeter. It is found that ICA produces the most consistent results.

Components of cross-frequency modulation in health and disease.

Science.gov (United States)

Allen, Elena A; Liu, Jingyu; Kiehl, Kent A; Gelernter, Joel; Pearlson, Godfrey D; Perrone-Bizzozero, Nora I; Calhoun, Vince D

2011-01-01

The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation (cfM), the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cfM from multi-channel EEG recordings acquired while schizophrenia patients (n = 47) and healthy controls (n = 130) performed an auditory oddball task. Novel application of independent component analysis (ICA) to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed covariation with diagnosis. Global cfM was significantly greater in healthy controls (F(1,175) = 9.25, P modulation at fronto-temporal electrodes was greater in patients (F(1,175) = 17.5, P modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABA(A) receptor (GABRA2) as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01). Overall, our results indicate great promise for this approach in establishing patterns of cfM in health and disease and elucidating the roles of oscillatory interactions in functional connectivity.

A solar station in Ica - Mutsumi Ishitsuka: a research center to improve education at the university and schools

Science.gov (United States)

Terrazas-Ramos, Raúl

2012-07-01

The San Luis Gonzaga National University of Ica has built a solar station, in collaboration with the Geophysical Institute of Peru, the National Astronomical Observatory of Japan and the Hida Observatory. The Solar Station has the following equipment: a digital Spectrograph Solar Refractor Telescope Takahashi 15 cm aperture, 60 cm reflector telescope aperture, a magnetometer-MAGDAS/CPNM and a Burst Monitor Telescope Solar-FMT (Project CHAIN). These teams support the development of astronomical science and Ica in Peru, likewise contributing to science worldwide. The development of basic science will be guaranteed when university students, professors and researchers work together. The Solar Station will be useful for studying the different levels of university education and also for the general public. The Solar Station will be a good way to spread science in the region through public disclosure.

COPD phenotype description using principal components analysis

DEFF Research Database (Denmark)

Roy, Kay; Smith, Jacky; Kolsum, Umme

2009-01-01

BACKGROUND: Airway inflammation in COPD can be measured using biomarkers such as induced sputum and Fe(NO). This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal components analysis (PCA). SUBJECTS...... AND METHODS: In 127 COPD patients (mean FEV1 61%), pulmonary function, Fe(NO), plasma CRP and TNF-alpha, sputum differential cell counts and sputum IL8 (pg/ml) were measured. Principal components analysis as well as multivariate analysis was performed. RESULTS: PCA identified four main components (% variance...... associations between the variables within components 1 and 2. CONCLUSION: COPD is a multi dimensional disease. Unrelated components of disease were identified, including neutrophilic airway inflammation which was associated with systemic inflammation, and sputum eosinophils which were related to increased Fe...

Color Doppler, power Doppler and B-flow ultrasound in the assessment of ICA stenosis: Comparison with 64-MD-CT angiography

International Nuclear Information System (INIS)

Clevert, D.A.; Johnson, T.; Flach, P.M.; Strautz, T.I.; Becker, C.; Reiser, M.; Jung, E.M.; Ritter, G.; Gallegos, M.T.; Kubale, R.

2007-01-01

The purpose of this study is to investigate the diagnostic potential of color-coded Doppler sonography (CCDS), power-Doppler (PD) and B-flow ultrasound in assessing the degree of extracranial internal carotid artery (ICA) stenosis in comparison to CT-angiography (MD-CTA). Thirty-two consecutive patients referred for CTA with 41 ICA-stenoses were included in this prospective study. MD-CTA was performed using a 64 row scanner with a CTDIvol of 13.1 mGy/cm. In CTA, CCDS, PD and B-flow, the degree of stenosis was evaluated by the minimal intrastenotic diameter in comparison to the poststenotic diameter. Two radiologists performed a quantitative evaluation of the stenoses in consensus blinded to the results of ultrasound. These were correlated to CTA, CCDS, PD and B-flow, intraoperative findings and clinical follow-up. Grading of the stenoses in B-flow ultrasound outperformed the other techniques in terms of accuracy with a correlation coefficient to CTA of 0.88, while PD and CCDS measurements yield coefficients of 0.74 and 0.70. Bland-Altman analysis additionally shows a very little bias of the three US methods between 0.5 and 3.2 %. There is excellent correlation (coefficient 0.88, CI 0.77-0.93) with 64-MD-CTA and B-flow ultrasound in terms of accuracy for intrastenotic and poststenotic diameter. Duplex sonography is useful for screening purposes. (orig.)

Component evaluation testing and analysis algorithms.

Energy Technology Data Exchange (ETDEWEB)

Hart, Darren M.; Merchant, Bion John

2011-10-01

The Ground-Based Monitoring R&E Component Evaluation project performs testing on the hardware components that make up Seismic and Infrasound monitoring systems. The majority of the testing is focused on the Digital Waveform Recorder (DWR), Seismic Sensor, and Infrasound Sensor. In order to guarantee consistency, traceability, and visibility into the results of the testing process, it is necessary to document the test and analysis procedures that are in place. Other reports document the testing procedures that are in place (Kromer, 2007). This document serves to provide a comprehensive overview of the analysis and the algorithms that are applied to the Component Evaluation testing. A brief summary of each test is included to provide the context for the analysis that is to be performed.

Improvement of Binary Analysis Components in Automated Malware Analysis Framework

Science.gov (United States)

2017-02-21

AFRL-AFOSR-JP-TR-2017-0018 Improvement of Binary Analysis Components in Automated Malware Analysis Framework Keiji Takeda KEIO UNIVERSITY Final...TYPE Final 3. DATES COVERED (From - To) 26 May 2015 to 25 Nov 2016 4. TITLE AND SUBTITLE Improvement of Binary Analysis Components in Automated Malware ...analyze malicious software ( malware ) with minimum human interaction. The system autonomously analyze malware samples by analyzing malware binary program

Formas de tierra y clases de pendiente del departamento de Ica: informe ejecutivo

OpenAIRE

Instituto Nacional de Recursos Naturales. Dirección General de Estudios y Proyectos

2000-01-01

Abarca la evaluación de las formas de tierra y clases de pendiente de la región sierra del departamento de Ica. El objetivo principal es brindar de manera esquemática el conjunto de formas de tierra, clases de pendiente dominante, sus diferencias morfoclimáticas y evolutivas, así como la identificación de las principales acciones erosivas que actualmente puedan significar riesgos a las actividades humanas, o a coadyuvar al deterioro generalizado del medio. El documento ha sido elaborado en el...

Combination of Resting state fMRI, DTI and sMRI Data to Discriminate Schizophrenia by N-way MCCA+jICA

Directory of Open Access Journals (Sweden)

Jing eSui

2013-05-01

Full Text Available Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called mCCA+jICA" as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF, grey matter density (GM and DTI (fractional anisotropy, FA data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n=35 relative to healthy controls (HCs, n=28. Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for 7 modality-combinations, showing the potential for a broad applicability of the mCCA+jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS, suggesting that gray matter density changes in default model network along with white matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of schizophrenia.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Science.gov (United States)

Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

PASCAL, Probabilistic Fracture Mechanics Analysis of Structural Components in Aging LWR

International Nuclear Information System (INIS)

Shibata, Katsuyuki; Onizawa, Kunio; Li, Yinsheng; Kato, Daisuke

2005-01-01

A - Description of program or function: PASCAL (PFM analysis of Structural Components in Aging LWR) is a PFM (Probabilistic Fracture Mechanics) code for evaluating the failure probability of aged pressure components. PASCAL has been developed as a part of the JAERI's research program on aging and structural integrity of LWR components, in order to respond to the increasing need of the probabilistic methodology in the regulation and inspection of nuclear components with the objective to provide a rational tool for the evaluation of the reliability and integrity of structural components. In order to improve the accuracy and reliability of the analysis code, some new fracture mechanics models or computational techniques are introduced considering the recent progress in the state of the art and performance of PC. Thus some new analysis models and original methodologies were introduced in PASCAL such as the elastic-plastic fracture criterion based on R6 method, a new crack extension model of semi-elliptical crack evaluation and so on. Moreover a function to evaluate the effect of embrittlement recovery by annealing of irradiated RPV is also introduced in the code based on the USNRC R.G. 1.162(1996). The code has been verified through various failure analysis results and international PTS round robin analysis ICAS which had been organized by the Principal Working Group 3 of OECD/NEA/CSNI. In order to attain a high usability, PASCAL Ver.1 with GUI provides an exclusive FEM pre-processor Pre-PASCAL for generating the input load transient data, a GUI system for generating the input data for PASCAL main processor of main solver and post-processor for output data. - Pre-PASCAL: Pre-PASCAL is an exclusive 3-D FEM pre-processor for generating the input transient data provided with 3 RPV mesh models and two simple specimen mesh models, i.e. CT and CCP. Almost the same input data format with that of PASCAL main processor is used. Output data of temperature and stress distribution

ICA contribution to the development of international standards of competence for nautical cartographers

Science.gov (United States)

Furness, Ron; Tsoulos, Lysandros

2018-05-01

All professions face challenges as how best to ensure the achievement and continuance of the highest standards as they seek to determine and promulgate best practices. In the essentially linked professions of hydrographic surveying and nautical cartography these challenges become immediately international in their impacts and require close cooperation between the professional bodies representing surveyors, hydrographers and cartographers. The Standards of Competence for Nautical Cartographers are known in short form as S-8 and they describe the minimum required competencies for nautical cartographers. They indicate the minimum degree of knowledge and experience considered necessary for nautical cartographers and provide a set of programme outlines against which the FIG/IHO/ICA International Board on Standards of Competence for Hydrographic Surveyors and Nautical Cartographers may evaluate programmes submitted for recognition. The Standards recognize two levels of competence: Cate- gory A and Category B. In nautical charting communities, Category A programmes offer levels of comprehensive and broad-based knowledge in all aspects of the theory and practice of nautical cartography. Category B programmes provide a level of practical comprehension, along with the essential theoretical background, necessary for individuals to carry out the various nautical cartography tasks. The Standards have recently undergone complete review and should be ratified during 2017/18. This paper aims to bring the existence and aims of the Standards to the wider notice of ICA members and to describe the philosophy and aims of the review in meeting the professional competency needs of the nautical cartographic community.

Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping.

Directory of Open Access Journals (Sweden)

Alexei eOssadtchi

2014-01-01

Full Text Available Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis.In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information Spectrum and is based on gauging the amount of mutual information of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of mutual information. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described mutual information based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the Mutual Information Spectrum for the selection of task-related independent components from real MEG data. We show that the Mutual Information spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.

Bayesian Independent Component Analysis

DEFF Research Database (Denmark)

Winther, Ole; Petersen, Kaare Brandt

2007-01-01

In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...

Key components of financial-analysis education for clinical nurses.

Science.gov (United States)

Lim, Ji Young; Noh, Wonjung

2015-09-01

In this study, we identified key components of financial-analysis education for clinical nurses. We used a literature review, focus group discussions, and a content validity index survey to develop key components of financial-analysis education. First, a wide range of references were reviewed, and 55 financial-analysis education components were gathered. Second, two focus group discussions were performed; the participants were 11 nurses who had worked for more than 3 years in a hospital, and nine components were agreed upon. Third, 12 professionals, including professors, nurse executive, nurse managers, and an accountant, participated in the content validity index. Finally, six key components of financial-analysis education were selected. These key components were as follows: understanding the need for financial analysis, introduction to financial analysis, reading and implementing balance sheets, reading and implementing income statements, understanding the concepts of financial ratios, and interpretation and practice of financial ratio analysis. The results of this study will be used to develop an education program to increase financial-management competency among clinical nurses. © 2015 Wiley Publishing Asia Pty Ltd.

Euler principal component analysis

NARCIS (Netherlands)

Liwicki, Stephan; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

Principal Component Analysis (PCA) is perhaps the most prominent learning tool for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2-norm employed by standard PCA is not robust to outliers. In this paper, we propose a kernel PCA method for fast and robust PCA,

QUALIDADE PROTÉICA DE DIVERSOS ALIMENTOS, INCLUINDO DIFERENTES VARIEDADES DE SOJA*

Directory of Open Access Journals (Sweden)

FABRíCIA QUEIROZ MENDES

2009-09-01

Full Text Available

O objetivo desse trabalho foi avaliar a digestibilidade protéica, o PER e NPR das seguintes fontes proteicas: caseína, albumina, arroz, aveia, carne de frango, peixe e suína, leite em pó, proteína de soro de leite, quinoa e cinco variedades de soja. A avaliação da qualidade protéica foi conduzida por ensaios biológicos, durante 14 dias, utilizando-se ratos recém desmamados. As dietas continham teores de 9,5% de proteína e 7% para a de arroz. Foi determinada a atividade de inibidor de tripsina nas amostras de soja. As dietas de caseína, albumina, arroz, proteína de soro de leite e as carnes suína, peixe e frango apresentaram maiores valores de digestibilidade, não diferindo entre si, com valores entre 92,12% a 95,54%. As que continham soja foram as que apresentaram menor valor de digestibilidade (73,23% a 78,04%. Para valores de PER e NPR, leite em pó, caseína, albumina, proteína de soro e as carnes apresentaram os maiores valores. Os valores encontrados para a inibição de tripsina variaram de 100,71 a 157,72 mg de tripsina inibida/g de proteína. A menor digestibilidade das variedades de soja prova que a presença de fatores antinutricionais reduz a biodisponibilidade das proteínas.

Identifying the Component Structure of Satisfaction Scales by Nonlinear Principal Components Analysis

NARCIS (Netherlands)

Manisera, M.; Kooij, A.J. van der; Dusseldorp, E.

2010-01-01

The component structure of 14 Likert-type items measuring different aspects of job satisfaction was investigated using nonlinear Principal Components Analysis (NLPCA). NLPCA allows for analyzing these items at an ordinal or interval level. The participants were 2066 workers from five types of social

Automatic selection of resting-state networks with functional magnetic resonance imaging

Directory of Open Access Journals (Sweden)

Silvia Francesca eStorti

2013-05-01

Full Text Available Functional magnetic resonance imaging (fMRI during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set. Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93% and precision (90%, 90%, 84%. The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥ 92% and the occipital network, which includes the medial visual cortical areas (≥ 94%, and consistent for the attention network (≥ 80%, the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥ 80%. The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA

Constrained principal component analysis and related techniques

CERN Document Server

Takane, Yoshio

2013-01-01

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concre

Analysis Method for Integrating Components of Product

Energy Technology Data Exchange (ETDEWEB)

Choi, Jun Ho [Inzest Co. Ltd, Seoul (Korea, Republic of); Lee, Kun Sang [Kookmin Univ., Seoul (Korea, Republic of)

2017-04-15

This paper presents some of the methods used to incorporate the parts constituting a product. A new relation function concept and its structure are introduced to analyze the relationships of component parts. This relation function has three types of information, which can be used to establish a relation function structure. The relation function structure of the analysis criteria was established to analyze and present the data. The priority components determined by the analysis criteria can be integrated. The analysis criteria were divided based on their number and orientation, as well as their direct or indirect characteristic feature. This paper presents a design algorithm for component integration. This algorithm was applied to actual products, and the components inside the product were integrated. Therefore, the proposed algorithm was used to conduct research to improve the brake discs for bicycles. As a result, an improved product similar to the related function structure was actually created.

Analysis Method for Integrating Components of Product

International Nuclear Information System (INIS)

Choi, Jun Ho; Lee, Kun Sang

2017-01-01

This paper presents some of the methods used to incorporate the parts constituting a product. A new relation function concept and its structure are introduced to analyze the relationships of component parts. This relation function has three types of information, which can be used to establish a relation function structure. The relation function structure of the analysis criteria was established to analyze and present the data. The priority components determined by the analysis criteria can be integrated. The analysis criteria were divided based on their number and orientation, as well as their direct or indirect characteristic feature. This paper presents a design algorithm for component integration. This algorithm was applied to actual products, and the components inside the product were integrated. Therefore, the proposed algorithm was used to conduct research to improve the brake discs for bicycles. As a result, an improved product similar to the related function structure was actually created.

Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

International Nuclear Information System (INIS)

Zeng, J.; Li, G.; Sun, J.

2013-01-01

Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)

Third nerve palsy caused by compression of the posterior communicating artery aneurysm does not depend on the size of the aneurysm, but on the distance between the ICA and the anterior-posterior clinoid process.

Science.gov (United States)

Anan, Mitsuhiro; Nagai, Yasuyuki; Fudaba, Hirotaka; Kubo, Takeshi; Ishii, Keisuke; Murata, Kumi; Hisamitsu, Yoshinori; Kawano, Yoshihisa; Hori, Yuzo; Nagatomi, Hirofumi; Abe, Tatsuya; Fujiki, Minoru

2014-08-01

Third nerve palsy (TNP) caused by a posterior communicating artery (PCoA) aneurysm is a well-known symptom of the condition, but the characteristics of unruptured PCoA aneurysm-associated third nerve palsy have not been fully evaluated. The aim of this study was to analyze the anatomical features of PCoA aneurysms that caused TNP from the viewpoint of the relationship between the ICA and the skull base. Forty-eight unruptured PCoA aneurysms were treated surgically between January 2008 and September 2013. The characteristics of the aneurysms were evaluated. Thirteen of the 48 patients (27%) had a history of TNP. The distance between the ICA and the anterior-posterior clinoid process (ICA-APC distance) was significantly shorter in the TNP group (pPCoA aneurysms can cause third nerve palsy if the ICA runs close to the skull base. Copyright © 2014 Elsevier B.V. All rights reserved.

Principal components analysis in clinical studies.

Science.gov (United States)

Zhang, Zhongheng; Castelló, Adela

2017-09-01

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

Objective selection of EEG late potentials through residual dependence estimation of independent components

International Nuclear Information System (INIS)

Milanesi, M; James, C J; Martini, N; Menicucci, D; Gemignani, A; Ghelarducci, B; Landini, L

2009-01-01

This paper presents a novel method to objectively select electroencephalographic (EEG) cortical sources estimated by independent component analysis (ICA) in event-related potential (ERP) studies. A proximity measure based on mutual information is employed to estimate residual dependences of the components that are then hierarchically clustered based on these residual dependences. Next, the properties of each group of components are evaluated at each level of the hierarchical tree by two indices that aim to assess both cluster tightness and physiological reliability through a template matching process. These two indices are combined in three different approaches to bring to light the hierarchical structure of the cluster organizations. Our method is tested on a set of experiments with the purpose of enhancing late positive ERPs elicited by emotional picture stimuli. Results suggest that the best way to look for physiologically plausible late positive potential (LPP) sources is to explore in depth the tightness of those clusters that, taken together, best resemble the template. According to our results, after brain sources clustering, LPPs are always identified more accurately than from ensemble-averaged raw data. Since the late components of an ERP involve the same associative areas, regardless of the modality of stimulation or specific tasks administered, the proposed method can be simply adapted to other ERP studies, and extended from psychophysiological studies to pathological or sport training evaluation support

FUZZY FUSION OF PCA, ICA AND ILDA FACE ALGORITHMS FOR ENHANCED USER AUTHENTICATION

Directory of Open Access Journals (Sweden)

PRASHANT KUMAR JAIN

2017-09-01

Full Text Available Use of biometrics has increased over last few years due to its inherent advantages over customary identification tools such as token card and password, etc. In biometrics, after fingerprint, face recognition is second most preferred method with reasonably good accuracy. In some applications like CCTV cameras where face of a person is available for processing, face recognition techniques can to be very useful. In this paper, integration of face recognition techniques PCA, ICA and ILDA using fuzzy fusion method is detailed. The preliminary results clearly reveal that the fusion of methods improves the accuracy of the user identification.

Hydrogen production from palm kernel shell via integrated catalytic adsorption (ICA) steam gasification

International Nuclear Information System (INIS)

Khan, Zakir; Yusup, Suzana; Ahmad, Murni Melati; Chin, Bridgid Lai Fui

2014-01-01

Highlights: • The paper presents integrated catalytic adsorption (ICA) steam gasification for H 2 yield. • Effects of adsorbent to biomass, biomass particle size and fluidization velocity on H 2 yield are examined. • The present study produces higher H 2 yield as compared to that obtained in literatures. • The ICA provides enhancement of H 2 yield as compared to independent catalytic and CO 2 adsorption gasification systems. - Abstract: The present study investigates the integrated catalytic adsorption (ICA) steam gasification of palm kernel shell for hydrogen production in a pilot scale atmospheric fluidized bed gasifier. The biomass steam gasification is performed in the presence of an adsorbent and a catalyst in the system. The effect of adsorbent to biomass (A/B) ratio (0.5–1.5 wt/wt), fluidization velocity (0.15–0.26 m/s) and biomass particle size (0.355–2.0 mm) are studied at temperature of 675 °C, steam to biomass (S/B) ratio of 2.0 (wt/wt) and biomass to catalyst ratio of 0.1 (wt/wt). Hydrogen composition and yield, total gas yield, and lower product gas heating values (LHV gas ) increases with increasing A/B ratio, while particle size has no significant effect on hydrogen composition and yield, total gas and char yield, gasification and carbon conversion efficiency. However, gas heating values increased with increasing biomass particle size which is due to presence of high methane content in product gas. Meanwhile, medium fluidization velocity of 0.21 m/s favoured hydrogen composition and yield. The results showed that the maximum hydrogen composition and yield of 84.62 vol% and 91.11 g H 2 /kg biomass are observed at A/B ratio of 1.5, S/B ratio of 2.0, catalyst to biomass ratio of 0.1 and temperature of 675 °C. The product gas heating values are observed in the range of 10.92–17.02 MJ/N m 3 . Gasification and carbon conversion efficiency are observed in the range of 25.66–42.95% and 20.61–41.95%, respectively. These lower

A robust physiology-based source separation method for QRS detection in low amplitude fetal ECG recordings

International Nuclear Information System (INIS)

Vullings, R; Bergmans, J W M; Peters, C H L; Hermans, M J M; Wijn, P F F; Oei, S G

2010-01-01

The use of the non-invasively obtained fetal electrocardiogram (ECG) in fetal monitoring is complicated by the low signal-to-noise ratio (SNR) of ECG signals. Even after removal of the predominant interference (i.e. the maternal ECG), the SNR is generally too low for medical diagnostics, and hence additional signal processing is still required. To this end, several methods for exploiting the spatial correlation of multi-channel fetal ECG recordings from the maternal abdomen have been proposed in the literature, of which principal component analysis (PCA) and independent component analysis (ICA) are the most prominent. Both PCA and ICA, however, suffer from the drawback that they are blind source separation (BSS) techniques and as such suboptimum in that they do not consider a priori knowledge on the abdominal electrode configuration and fetal heart activity. In this paper we propose a source separation technique that is based on the physiology of the fetal heart and on the knowledge of the electrode configuration. This technique operates by calculating the spatial fetal vectorcardiogram (VCG) and approximating the VCG for several overlayed heartbeats by an ellipse. By subsequently projecting the VCG onto the long axis of this ellipse, a source signal of the fetal ECG can be obtained. To evaluate the developed technique, its performance is compared to that of both PCA and ICA and to that of augmented versions of these techniques (aPCA and aICA; PCA and ICA applied on preprocessed signals) in generating a fetal ECG source signal with enhanced SNR that can be used to detect fetal QRS complexes. The evaluation shows that the developed source separation technique performs slightly better than aPCA and aICA and outperforms PCA and ICA and has the main advantage that, with respect to aPCA/PCA and aICA/ICA, it performs more robustly. This advantage renders it favorable for employment in automated, real-time fetal monitoring applications

Differentiating emotional responses to images and words

DEFF Research Database (Denmark)

Jensen, Camilla Birgitte Falk; Petersen, Michael Kai; Larsen, Jakob Eg

responses are characterized by only small voltage changes that have typically been found in group studies involving multiple trials and large numbers of participants. Hypothesizing that spatial filtering might enhance retrieval, we apply independent component analysis (ICA) to cluster scalp maps and time...... series responses in a single subject based on only a few trials. Comparing our results against previous findings we identify multiple early and late ICA components that are similarly modulated by neutral, pleasant and unpleasant content in both images and words. Suggesting that we might be able to model...

Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

Directory of Open Access Journals (Sweden)

Chang Liu

2014-01-01

Full Text Available To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA, incremental principal component analysis (IPCA, and multilinear principal component analysis (MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.

PREFACE: International Conference on Applied Sciences 2015 (ICAS2015)

Science.gov (United States)

Lemle, Ludovic Dan; Jiang, Yiwen

2016-02-01

The International Conference on Applied Sciences ICAS2015 took place in Wuhan, China on June 3-5, 2015 at the Military Economics Academy of Wuhan. The conference is regularly organized, alternatively in Romania and in P.R. China, by Politehnica University of Timişoara, Romania, and Military Economics Academy of Wuhan, P.R. China, with the joint aims to serve as a platform for exchange of information between various areas of applied sciences, and to promote the communication between the scientists of different nations, countries and continents. The topics of the conference cover a comprehensive spectrum of issues from: >Economical Sciences and Defense: Management Sciences, Business Management, Financial Management, Logistics, Human Resources, Crisis Management, Risk Management, Quality Control, Analysis and Prediction, Government Expenditure, Computational Methods in Economics, Military Sciences, National Security, and others... >Fundamental Sciences and Engineering: Interdisciplinary applications of physics, Numerical approximation and analysis, Computational Methods in Engineering, Metallic Materials, Composite Materials, Metal Alloys, Metallurgy, Heat Transfer, Mechanical Engineering, Mechatronics, Reliability, Electrical Engineering, Circuits and Systems, Signal Processing, Software Engineering, Data Bases, Modeling and Simulation, and others... The conference gathered qualified researchers whose expertise can be used to develop new engineering knowledge that has applicability potential in Engineering, Economics, Defense, etc. The number of participants was 120 from 11 countries (China, Romania, Taiwan, Korea, Denmark, France, Italy, Spain, USA, Jamaica, and Bosnia and Herzegovina). During the three days of the conference four invited and 67 oral talks were delivered. Based on the work presented at the conference, 38 selected papers have been included in this volume of IOP Conference Series: Materials Science and Engineering. These papers present new research

PREFACE: Fullerene Nano Materials (Symposium of IUMRS-ICA2008)

Science.gov (United States)

Miyazawa, Kun'ichi; Fujita, Daisuke; Wakahara, Takatsugu; Kizuka, Tokushi; Matsuishi, Kiyoto; Ochiai, Yuichi; Tachibana, Masaru; Ogata, Hironori; Mashino, Tadahiko; Kumashiro, Ryotaro; Oikawa, Hidetoshi

2009-07-01

This volume contains peer-reviewed invited and contributed papers that were presented in Symposium N 'Fullerene Nano Materials' at the IUMRS International Conference in Asia 2008 (IUMRS-ICA 2008), which was held on 9-13 December 2008, at Nagoya Congress Center, Nagoya, Japan. Over twenty years have passed since the discovery of C60 in 1985. The discovery of superconductivity of C60 in 1991 suggested infinite possibilities for fullerenes. On the other hand, a new field of nanocarbon has been developed recently, based on novel functions of the low-dimensional fullerene nanomaterials that include fullerene nanowhiskers, fullerene nanotubes, fullerene nanosheets, chemically modified fullerenes, endohedral fullerenes, thin films of fullerenes and so forth. Electrical, electrochemical, optical, thermal, mechanical and various other properties of fullerene nanomaterials have been investigated and their novel and anomalous nature has been reported. Biological properties of fullerene nanomaterials also have been investigated both in medical applications and toxicity aspects. The recent research developments of fullerene nanomaterials cover a variety of categories owing to their functional diversity. This symposium aimed to review the progress in the state-of-the-art technology based on fullerenes and to offer the forum for active interdisciplinary discussions. 24 oral papers containing 8 invited papers and 22 poster papers were presented at the two-day symposium. Topics on the social acceptance of nanomaterials including fullerene were presented on the first day of the symposium. Biological impacts of nanomaterials and the importance of standardization of nanomaterials characterization were also shown. On the second day, the synthesis, properties, functions and applications of various fullerene nanomaterials were shown in both the oral and poster presentations. We are grateful to all invited speakers and many participants for valuable contributions and active discussions

Structured Performance Analysis for Component Based Systems

OpenAIRE

Salmi , N.; Moreaux , Patrice; Ioualalen , M.

2012-01-01

International audience; The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. % This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed...

The Ica-Atom using as access platform, dissemination and description of archival documents public institutions

Directory of Open Access Journals (Sweden)

Eliseu dos Santos Lima

2016-12-01

Full Text Available Introduction: Information technology has caused changes in various segments of society. In archival area its presence is remarkable, so much that lately has increased the quantity of institutions that make use of computerized systems, repositories and platforms that automate activities such as description, diffusion, and even provide access to archival documentary heritage. Objective: To discuss the functionality and usefulness of the platform International Archives Council - Access to Memory (ICA-AtoM in archival description of activities, dissemination and access to archival documentary heritage. Methodology: bibliographical research, drawn from the archival legislation, especially through research resulting from Master's dissertations, articles published in journals of the Science field of Information and manuals produced by members of the Group of CNPq-UFSM Research: Electronic Management of archival documents - Ged / A. Results: It was observed that the platform is used to describe both the permanent documents, and to provide access to information recorded in current and intermediate documents, in order to streamline access to documentary resources of public institutions. Conclusions: It is considered that the ICA-Atom besides serving the description, can provide an authentic environment for the secure storage and as a source of evidence of digital and digitized institutional documents, as well as to provide for preservation and long term access if It is connected to a RDC-Arq, like Archivematica, for example.

Blind source separation problem in GPS time series

Science.gov (United States)

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

2016-04-01

A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind source separation (BSS) problem, i.e., in recovering and separating the original sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an L2 norm (χ 2), looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic source. We evaluate the ability of the PCA and ICA decomposition

recA mediated spontaneous deletions of the icaADBC operon of clinical Staphylococcus epidermidis isolates : a new mechanism of phenotypic variations

NARCIS (Netherlands)

Nuryastuti, Titik; van der Mei, Henny C.; Busscher, Henk J.; Kuijer, Roel; Aman, Abu T.; Krom, Bastiaan P.

Phenotypic variation of Staphylococcus epidermidis involving the slime related ica operon results in heterogeneity in surface characteristics of individual bacteria in axenic cultures. Five clinical S. epidermidis isolates demonstrated phenotypic variation, i.e. both black and red colonies on Congo

ECG-derived respiration methods: adapted ICA and PCA.

Science.gov (United States)

Tiinanen, Suvi; Noponen, Kai; Tulppo, Mikko; Kiviniemi, Antti; Seppänen, Tapio

2015-05-01

Respiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI.

Science.gov (United States)

Sair, Haris I; Yahyavi-Firouz-Abadi, Noushin; Calhoun, Vince D; Airan, Raag D; Agarwal, Shruti; Intrapiromkul, Jarunee; Choe, Ann S; Gujar, Sachin K; Caffo, Brian; Lindquist, Martin A; Pillai, Jay J

2016-03-01

To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance. Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression. Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50. Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks. © 2015 Wiley Periodicals, Inc.

Multilevel sparse functional principal component analysis.

Science.gov (United States)

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

2014-01-29

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

Components of cross-frequency modulation in health and disease

Directory of Open Access Journals (Sweden)

Elena A Allen

2011-07-01

Full Text Available The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation, the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cross-frequency modulation from multi-channel EEG recordings acquired while schizophrenia patients (n = 47 and healthy controls (n = 130 performed an auditory oddball task. Novel application of independent component analysis (ICA to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed co-variation with diagnosis. Global cross-frequency modulation was significantly greater in healthy controls (F1,175=9.25, P<0.005, while modulation at fronto-temporal electrodes was greater in patients (F1,175 =17.5, P<0.0001. We further found that the weights of schizophrenia-relevant components were associated with genetic polymorphisms at previously identified risk loci. Global cross-frequency modulation decreased with copies of 957C allele in the gene for the dopamine D2 receptor (r = −0.20, P < 0.01 across all subjects. Additionally, greater ‘aberrant’ fronto-temporal modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABAA receptor (GABRA2 as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01. Overall, our results indicate great promise for this approach in establishing patterns of cross-frequency modulation in health and disease and elucidating the roles of oscillatory interactions in functional connectivity.

Signal-dependent independent component analysis by tunable mother wavelets

International Nuclear Information System (INIS)

Seo, Kyung Ho

2006-02-01

The objective of this study is to improve the standard independent component analysis when applied to real-world signals. Independent component analysis starts from the assumption that signals from different physical sources are statistically independent. But real-world signals such as EEG, ECG, MEG, and fMRI signals are not statistically independent perfectly. By definition, standard independent component analysis algorithms are not able to estimate statistically dependent sources, that is, when the assumption of independence does not hold. Therefore before independent component analysis, some preprocessing stage is needed. This paper started from simple intuition that wavelet transformed source signals by 'well-tuned' mother wavelet will be simplified sufficiently, and then the source separation will show better results. By the correlation coefficient method, the tuning process between source signal and tunable mother wavelet was executed. Gamma component of raw EEG signal was set to target signal, and wavelet transform was executed by tuned mother wavelet and standard mother wavelets. Simulation results by these wavelets was shown

A novel approach for dimension reduction of microarray.

Science.gov (United States)

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

EXAFS and principal component analysis : a new shell game

International Nuclear Information System (INIS)

Wasserman, S.

1998-01-01

The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions

Problems of stress analysis of fuelling machine head components

International Nuclear Information System (INIS)

Mathur, D.D.

1975-01-01

The problem of stress analysis of fuelling machine head components are discussed. To fulfil the functional requirements, the components are required to have certain shapes where stress problems cannot be matched to a catalogue of pre-determined solutions. The areas where complex systems of loading due to hydrostatic pressure, weight, moments and temperature gradients coupled with the intricate shapes of the components make it difficult to arrive at satisfactory solutions. Particularly, the analysis requirements of the magazine housing, end cover, gravloc clamps and centre support are highlighted. An experimental stress analysis programme together with a theoretical finite element analysis is perhaps the answer. (author)

CONSTITUYENTES VOLÁTILES DE DOS VARIEDADES DE GUAYABA (Psidium guajava L.: Palmira ICA-1 y Glum Sali

Directory of Open Access Journals (Sweden)

Clara E Quijano C

2010-08-01

Full Text Available Los constituyentes volátiles de la pulpa de dos variedades de guayaba (Psidium guajava L (Palmira ICA-1 y Glum Salí, fueron obtenidos por extracción L-L (pentano: diclorometano 1:1; los extractos concentrados se fraccionaron por Cromatografía de Columna y se analizaron por CGAR Y CGAR-EM. De la variedad Palmira - ICA-1 se identificaron 86 compuestos, siendo mayoritarios el acetato de cinamilo, el acetato de (Z 3-hexenilo y el alcohol cinnamílico. En la variedad Glum Sali, se determinaron 77 constituyentes, de los cuales predominaron cuantitativamente el (E-2-hexenal, el hexanal y el tetradecano. La principal diferencia en la composición de volátiles entre las dos variedades es el alto contenido de esteres en la primera variedad, mientras que en la Glum Sali predominaron los hidrocarburos y los aldehidos C(,. De los compuestos volátiles identificados en este trabajo, la 4- hidroxi-4-metil-2-pentanona, la 3-heptanona, el 3-hidroxibutanoato de etilo, el acetato de 2-hidroxietilo, el 1,2-propanodiol, el 1,2-etanodiol, el acetato de 3-metilbutilo y el 3-metil-2 butanol son reportados por primera vez como constituyentes del aroma de guayaba.

Violence-related content in video game may lead to functional connectivity changes in brain networks as revealed by fMRI-ICA in young men.

Science.gov (United States)

Zvyagintsev, M; Klasen, M; Weber, R; Sarkheil, P; Esposito, F; Mathiak, K A; Schwenzer, M; Mathiak, K

2016-04-21

In violent video games, players engage in virtual aggressive behaviors. Exposure to virtual aggressive behavior induces short-term changes in players' behavior. In a previous study, a violence-related version of the racing game "Carmageddon TDR2000" increased aggressive affects, cognitions, and behaviors compared to its non-violence-related version. This study investigates the differences in neural network activity during the playing of both versions of the video game. Functional magnetic resonance imaging (fMRI) recorded ongoing brain activity of 18 young men playing the violence-related and the non-violence-related version of the video game Carmageddon. Image time series were decomposed into functional connectivity (FC) patterns using independent component analysis (ICA) and template-matching yielded a mapping to established functional brain networks. The FC patterns revealed a decrease in connectivity within 6 brain networks during the violence-related compared to the non-violence-related condition: three sensory-motor networks, the reward network, the default mode network (DMN), and the right-lateralized frontoparietal network. Playing violent racing games may change functional brain connectivity, in particular and even after controlling for event frequency, in the reward network and the DMN. These changes may underlie the short-term increase of aggressive affects, cognitions, and behaviors as observed after playing violent video games. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

A wearable neuro-feedback system with EEG-based mental status monitoring and transcranial electrical stimulation.

Science.gov (United States)

Roh, Taehwan; Song, Kiseok; Cho, Hyunwoo; Shin, Dongjoo; Yoo, Hoi-Jun

2014-12-01

A wearable neuro-feedback system is proposed with a low-power neuro-feedback SoC (NFS), which supports mental status monitoring with encephalography (EEG) and transcranial electrical stimulation (tES) for neuro-modulation. Self-configured independent component analysis (ICA) is implemented to accelerate source separation at low power. Moreover, an embedded support vector machine (SVM) enables online source classification, configuring the ICA accelerator adaptively depending on the types of the decomposed components. Owing to the hardwired accelerating functions, the NFS dissipates only 4.45 mW to yield 16 independent components. For non-invasive neuro-modulation, tES stimulation up to 2 mA is implemented on the SoC. The NFS is fabricated in 130-nm CMOS technology.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

Science.gov (United States)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Condition monitoring with Mean field independent components analysis

DEFF Research Database (Denmark)

Pontoppidan, Niels Henrik; Sigurdsson, Sigurdur; Larsen, Jan

2005-01-01

We discuss condition monitoring based on mean field independent components analysis of acoustic emission energy signals. Within this framework it is possible to formulate a generative model that explains the sources, their mixing and also the noise statistics of the observed signals. By using...... a novelty approach we may detect unseen faulty signals as indeed faulty with high precision, even though the model learns only from normal signals. This is done by evaluating the likelihood that the model generated the signals and adapting a simple threshold for decision. Acoustic emission energy signals...... from a large diesel engine is used to demonstrate this approach. The results show that mean field independent components analysis gives a better detection of fault compared to principal components analysis, while at the same time selecting a more compact model...

Integrated Phoneme Subspace Method for Speech Feature Extraction

Directory of Open Access Journals (Sweden)

Park Hyunsin

2009-01-01

Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

Abstract interfaces for data analysis - component architecture for data analysis tools

International Nuclear Information System (INIS)

Barrand, G.; Binko, P.; Doenszelmann, M.; Pfeiffer, A.; Johnson, A.

2001-01-01

The fast turnover of software technologies, in particular in the domain of interactivity (covering user interface and visualisation), makes it difficult for a small group of people to produce complete and polished software-tools before the underlying technologies make them obsolete. At the HepVis'99 workshop, a working group has been formed to improve the production of software tools for data analysis in HENP. Beside promoting a distributed development organisation, one goal of the group is to systematically design a set of abstract interfaces based on using modern OO analysis and OO design techniques. An initial domain analysis has come up with several categories (components) found in typical data analysis tools: Histograms, Ntuples, Functions, Vectors, Fitter, Plotter, analyzer and Controller. Special emphasis was put on reducing the couplings between the categories to a minimum, thus optimising re-use and maintainability of any component individually. The interfaces have been defined in Java and C++ and implementations exist in the form of libraries and tools using C++ (Anaphe/Lizard, OpenScientist) and Java (Java Analysis Studio). A special implementation aims at accessing the Java libraries (through their Abstract Interfaces) from C++. The authors give an overview of the architecture and design of the various components for data analysis as discussed in AIDA

Component reliability analysis for development of component reliability DB of Korean standard NPPs

International Nuclear Information System (INIS)

Choi, S. Y.; Han, S. H.; Kim, S. H.

2002-01-01

The reliability data of Korean NPP that reflects the plant specific characteristics is necessary for PSA and Risk Informed Application. We have performed a project to develop the component reliability DB and calculate the component reliability such as failure rate and unavailability. We have collected the component operation data and failure/repair data of Korean standard NPPs. We have analyzed failure data by developing a data analysis method which incorporates the domestic data situation. And then we have compared the reliability results with the generic data for the foreign NPPs

Incremental Capacity Analysis of a Lithium-Ion Battery Pack for Different Charging Rates

DEFF Research Database (Denmark)

Kalogiannis, Theodoros; Stroe, Daniel-Ioan; Nyborg, Jonas

2017-01-01

-depth investigation of two battery packs composed of 14 Lithium-ion cells each; for the purpose of evaluating the applicability and the challenges of the ICA on a battery pack level by means of different charging current rates. Also, at a certain charging current, the influence of the temperature on the ICA curves......Incremental Capacity Analysis (ICA) is a method used to investigate the capacity state of health of batteries by tracking the electrochemical properties of the cell. It is based on the differentiation of the battery capacity over the battery voltage, for a full or a partial cycle regarding...

Sparse Principal Component Analysis in Medical Shape Modeling

DEFF Research Database (Denmark)

Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

2006-01-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

Aplicación de índices de calidad de agua - ICA orientados al uso de la fuente para consumo humano

Directory of Open Access Journals (Sweden)

Patricia Torres

2010-09-01

Full Text Available La contaminación de origen natural y antrópico de las fuentes de agua limitan su aprovechamiento principalmente para el consumo humano; herramientas de diagnóstico rápidas y representativas como los índices de calidad del agua —ICA— garantizan una evaluación integral del recurso, fundamental en la toma de acciones para manejo y control del riesgo sanitario a través de los diferentes procesos de potabilización. El análisis comparativo de la aplicación de los índices ICA-NSF, ICA Dinius, ICAUCA y UWQI en cinco puntos del río Cauca localizados en el tramo Salvajina – Bocatoma Puerto Mallarino mostró que el río presenta un deterioro creciente a medida que es afectado por las actividades socioeconómicas desarrolladas en su cuenca, presentando calidad entre regular y mala, haciendo necesario su tratamiento para la destinación del recurso en consumo humano, e incluso la incorporación de tratamientos específicos como la adsorción con carbón activado y otros. Las variables de mayor incidencia en el valor final de los ICA fueron los patógenos y las asociadas a presencia de material particulado; sin embargo, para una evaluación más integral del río, y dados los usos del suelo que pueden generar en la fuente presencia de otras sustancias causantes de riesgo sanitario, se recomienda considerar el desarrollo o adaptación de un índice con estructura similar a la del DQWI que tenga en cuenta la variación en el tiempo y en el espacio de los parámetros que lo conforman y su comparación con la normatividad vigente.

Aplicação do ICA-AtoM no acervo fotográfico do departamento de arquivo geral da UFSM

Directory of Open Access Journals (Sweden)

Henrique Machado dos Santos

2017-04-01

Full Text Available A Arquivologia vem ganhando força com a adesão de novas tecnologias, isso faz o arquivista ser visto como um administrador de informações, mudando assim aquela imagem antiquada. Funções do profissional como a descrição, a preservação e a garantia de acesso à informação, podem ser realizadas com o auxílio de softwares específicos. Nesse contexto o Curso de Arquivologia da Universidade Federal de Santa Maria (UFSM juntamente ao Departamento de Arquivo Geral da UFSM, desenvolveram práticas aplicadas à instituição. Foi instalado o ICA-AtoM, possuidor dos requisitos desejados para atender as necessidades de descrição e difusão do acervo fotográfico ali existente. Essa ideia resultou em um projeto de extensão, que conta com bons resultados. Um dos fatores diferenciais do trabalho é que o ICA-AtoM é um software específico para suas atividades, não há adaptações do programa.

Fast linear method of illumination classification

Science.gov (United States)

Cooper, Ted J.; Baqai, Farhan A.

2003-01-01

We present a simple method for estimating the scene illuminant for images obtained by a Digital Still Camera (DSC). The proposed method utilizes basis vectors obtained from known memory color reflectance to identify the memory color objects in the image. Once the memory color pixels are identified, we use the ratios of the red/green and blue/green to determine the most likely illuminant in the image. The critical part of the method is to estimate the smallest set of basis vectors that closely represent the memory color reflectances. Basis vectors obtained from both Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used. We will show that only two ICA basis vectors are needed to get an acceptable estimate.

Cerebral hemodynamic changes measured by gradient-echo or spin-echo bolus tracking and its correlation to changes in ICA blood flow measured by phase-mapping MRI

DEFF Research Database (Denmark)

Marstrand, J.R.; Rostrup, Egill; Garde, Ellen

2001-01-01

Changes in cerebral blood flow (CBF) induced by Acetazolamide (ACZ) were measured using dynamic susceptibility contrast MRI (DSC-MRI) with both spin echo (SE) EPI and gradient echo (GE) EPI, and related to changes in internal carotid artery (ICA) flow measured by phase-mapping. Also examined...... increase in CBF and CBV in response to ACZ, while SE-EPI measured a significant increase in CBV and MTT. CBV and MTT change measured by SE-EPI was sensitive to previous bolus injections. There was a significant linear relation between change in CBF measured by GE-EPI and change in ICA flow. In conclusion......, GE-EPI under the present condition was superior to SE-EPI in monitoring cerebral vascular changes...

Dynamic Modal Analysis of Vertical Machining Centre Components

OpenAIRE

Anayet U. Patwari; Waleed F. Faris; A. K. M. Nurul Amin; S. K. Loh

2009-01-01

The paper presents a systematic procedure and details of the use of experimental and analytical modal analysis technique for structural dynamic evaluation processes of a vertical machining centre. The main results deal with assessment of the mode shape of the different components of the vertical machining centre. The simplified experimental modal analysis of different components of milling machine was carried out. This model of the different machine tool's structure is made by design software...

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

Directory of Open Access Journals (Sweden)

Zhiliang Wang

2014-01-01

Full Text Available The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA. Functional data analysis (FDA deals with random variables (or process with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50. Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors.

Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response

OpenAIRE

Sipp, Amy R.; Gwin, Joseph T.; Makeig, Scott; Ferris, Daniel P.

2013-01-01

Determining the neural correlates of loss of balance during walking could lead to improved clinical assessment and treatment for individuals predisposed to falls. We used high-density electroencephalography (EEG) combined with independent component analysis (ICA) to study loss of balance during human walking. We examined 26 healthy young subjects performing heel-to-toe walking on a treadmill-mounted balance beam as well as walking on the treadmill belt (both at 0.22 m/s). ICA identified clust...

Probabilistic Principal Component Analysis for Metabolomic Data.

LENUS (Irish Health Repository)

Nyamundanda, Gift

2010-11-23

Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Matsuo, Yukinori, E-mail: ymatsuo@kuhp.kyoto-u.ac.jp; Nakamura, Mitsuhiro; Mizowaki, Takashi; Hiraoka, Masahiro [Department of Radiation Oncology and Image-applied Therapy, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo, Kyoto 606-8507 (Japan)

2016-09-15

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

International Nuclear Information System (INIS)

Matsuo, Yukinori; Nakamura, Mitsuhiro; Mizowaki, Takashi; Hiraoka, Masahiro

2016-01-01

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.

Feature study of hysterical blindness EEG based on FastICA with combined-channel information.

Science.gov (United States)

Qin, Xuying; Wang, Wei; Hu, Lintao; Wang, Xu; Yuan, Xiaojie

2015-01-01

An appropriate feature study of hysteria electroencephalograms (EEG) would provide new insights into neural mechanisms of the disease, and also make improvements in patient diagnosis and management. The objective of this paper is to provide an explanation for what causes a particular visual loss, by associating the features of hysterical blindness EEG with brain function. An idea for the novel feature extraction for hysterical blindness EEG, utilizing combined-channel information, was applied in this paper. After channels had been combined, the sliding-window-FastICA was applied to process the combined normal EEG and hysteria EEG, respectively. Kurtosis features were calculated from the processed signals. As the comparison feature, the power spectral density of normal and hysteria EEG were computed. According to the feature analysis results, a region of brain dysfunction was located at the occipital lobe, O1 and O2. Furthermore, new abnormality was found at the parietal lobe, C3, C4, P3, and P4, that provided us with a new perspective for understanding hysterical blindness. Indicated by the kurtosis results which were consistent with brain function and the clinical diagnosis, our method was found to be a useful tool to capture features in hysterical blindness EEG.

Clinical usefulness of physiological components obtained by factor analysis

International Nuclear Information System (INIS)

Ohtake, Eiji; Murata, Hajime; Matsuda, Hirofumi; Yokoyama, Masao; Toyama, Hinako; Satoh, Tomohiko.

1989-01-01

The clinical usefulness of physiological components obtained by factor analysis was assessed in 99m Tc-DTPA renography. Using definite physiological components, another dynamic data could be analyzed. In this paper, the dynamic renal function after ESWL (Extracorporeal Shock Wave Lithotripsy) treatment was examined using physiological components in the kidney before ESWL and/or a normal kidney. We could easily evaluate the change of renal functions by this method. The usefulness of a new analysis using physiological components was summarized as follows: 1) The change of a dynamic function could be assessed in quantity as that of the contribution ratio. 2) The change of a sick condition could be morphologically evaluated as that of the functional image. (author)

Disease progression and search for monogenic diabetes among children with new onset type 1 diabetes negative for ICA, GAD- and IA-2 Antibodies

DEFF Research Database (Denmark)

Pörksen, Sven; Laborie, Lene Bjerke; Nielsen, Lotte

2010-01-01

BACKGROUND: To investigate disease progression the first 12 months after diagnosis in children with type 1 diabetes negative (AAB negative) for pancreatic autoantibodies [islet cell autoantibodies(ICA), glutamic acid decarboxylase antibodies (GADA) and insulinoma-associated antigen-2 antibodies (IA......-2A)]. Furthermore the study aimed at determining whether mutations in KCNJ11, ABCC8, HNF1A, HNF4A or INS are common in AAB negative diabetes. MATERIALS AND METHODS: In 261 newly diagnosed children with type 1 diabetes, we measured residual β-cell function, ICA, GADA, and IA-2A at 1, 6 and 12 months...... of arginine at residue 1530 of SUR1 (ABCC8) by cysteine. Functional analyses of recombinant K-ATP channels showed that R1530C markedly reduced the sensitivity of the K-ATP channel to inhibition by MgATP. Morover, the channel was highly sensitive to sulphonylureas. However, there was no effect of sulfonylurea...

Multi-component separation and analysis of bat echolocation calls.

Science.gov (United States)

DiCecco, John; Gaudette, Jason E; Simmons, James A

2013-01-01

The vast majority of animal vocalizations contain multiple frequency modulated (FM) components with varying amounts of non-linear modulation and harmonic instability. This is especially true of biosonar sounds where precise time-frequency templates are essential for neural information processing of echoes. Understanding the dynamic waveform design by bats and other echolocating animals may help to improve the efficacy of man-made sonar through biomimetic design. Bats are known to adapt their call structure based on the echolocation task, proximity to nearby objects, and density of acoustic clutter. To interpret the significance of these changes, a method was developed for component separation and analysis of biosonar waveforms. Techniques for imaging in the time-frequency plane are typically limited due to the uncertainty principle and interference cross terms. This problem is addressed by extending the use of the fractional Fourier transform to isolate each non-linear component for separate analysis. Once separated, empirical mode decomposition can be used to further examine each component. The Hilbert transform may then successfully extract detailed time-frequency information from each isolated component. This multi-component analysis method is applied to the sonar signals of four species of bats recorded in-flight by radiotelemetry along with a comparison of other common time-frequency representations.

Analysis methods for structure reliability of piping components

International Nuclear Information System (INIS)

Schimpfke, T.; Grebner, H.; Sievers, J.

2004-01-01

In the frame of the German reactor safety research program of the Federal Ministry of Economics and Labour (BMWA) GRS has started to develop an analysis code named PROST (PRObabilistic STructure analysis) for estimating the leak and break probabilities of piping systems in nuclear power plants. The long-term objective of this development is to provide failure probabilities of passive components for probabilistic safety analysis of nuclear power plants. Up to now the code can be used for calculating fatigue problems. The paper mentions the main capabilities and theoretical background of the present PROST development and presents some of the results of a benchmark analysis in the frame of the European project NURBIM (Nuclear Risk Based Inspection Methodologies for Passive Components). (orig.)

Aspergiloma Pulmonar en el Hospital de Apoyo Departamental de Ica - Perú. 2000 - 2001

Directory of Open Access Journals (Sweden)

Alicia Arce M

2002-10-01

Full Text Available En el Perú, un gran porcentaje de la población que tiene lesiones cavitarias residuales puede albergar una bola fúngica conocida como aspergiloma. Objetivo: determinar los agentes etiológicos que causan los aspergilomas en estas personas y comparar la prueba diagnóstica de inmunodifusión frente al cultivo seriado de esputo. Materiales y métodos: se incluyó a pacientes atendidos en el Programa de Control de Tuberculosis del Hospital Regional de Ica (Ica, Perú que presentaron antecedentes de tuberculosis pulmonar y criterios clínico-radiológicos sospechosos de aspergilosis pulmonar. El diagnóstico de laboratorio se realizó mediante cultivos seriados y consecutivos de esputo en Agar Sabouraud Dextrosa (ASD con cloramfenicol y se detectó precipitinas aspergilares con la prueba de inmunodifusión (ID Resultados: se obtuvo un total de 20 pacientes, 70% de los pacientes (14/20 demostraron tener aspergiloma pulmonar. Los principales agentes etiológicos encontrados fueron Aspergillus fumigatus (50% y Aspergillus níger (14,5% La ID mostró 71% de sensibilidad (aumentando este valor a 82% al utilizar antígeno específico y 100% de especificidad. Conclusiones: Aspergillus fumigatus es el agente etiológico más frecuente en nuestro estudio y la prueba de inmunodifusión es útil como prueba diagnóstica de aspergiloma pulmonar. La prueba de inmunodifusión mejora su sensibilidad al emplear antígenos específicos, por lo que consideramos realizar estudios de elaboración de antígenos específicos de Aspergillus autóctonos para la prueba de ID. Es necesario continuar estudios de prevalencia y de métodos diagnósticos de esta enfermedad.

System diagnostics using qualitative analysis and component functional classification

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.

1993-01-01

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures

Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach.

Science.gov (United States)

Jäncke, Lutz; Alahmadi, Nsreen

2016-01-01

In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical

Overview of evidence on emergency carotid stenting in patients with acute ischemic stroke due to tandem occlusions: a systematic review and meta-analysis.

Science.gov (United States)

Coelho, Andreia; Lobo, Miguel; Gouveia, Ricardo; Silveira, Diogo; Campos, Jacinta; Augusto, Rita; Coelho, Nuno; Canedo, Alexandra

2018-01-23

Endovascular intracranial thrombectomy (IT) has established itself as the standard of care in treating large-vessel anterior circulation acute ischemic stroke (AIS). However, internal carotid artery (ICA) stenosis/occlusion hampers distal access and controversy about simultaneous emergency ICA stenting ensues. The purpose of this review was to evaluate the safety of emergency ICA stenting in combination with IT for AIS with tandem occlusions. To our knowledge this is the first meta-analysis to evaluate emergency ICA stenting in tandem occlusions, combining results from studies with a control group. A systematic review and meta-analysis was conducted according to the recommendations of the Preferred Reporting Items for Systematic reviews and Meta- Analyses (PRISMA) statement. A total of 649 potentially relevant articles were initially selected. After reviewing at title or abstract level, 87 articles were read in full and 23 were included. These studies recruited 1000 patients, 220 submitted to IT with no emergency ICA stenting and 780 to IT and emergency ICA stenting. Successful revascularization (Thrombolysis in cerebral infarction scale - TICI≥2b) was achieved in 48.6-100%. Good outcome (modified Rankin scale - mRS≤2) ranged from 18.2-100%. Symptomatic intracranial haemorrhage (sICH) ranged from 0-45.7% (overall n=168; 17.2%). Mortality at 90 days ranged from 0-45.4% (overall n=114; 11.7%). Time to recanalization was significantly longer in the stenting group with an overall mean difference of 1.76 (95% Confidence Interval: 1.59-1.93). In this meta-analysis time to recanalization was significantly longer in the emergency ICA stenting group. There was no benefit from emergency stenting in parameters such as successful revascularization (TICI≥2b), clinical outcome (mRS≤2) or 90-day mortality. Data on sICH were scarce. Emergency ICA stenting appears to increase time to revascularization and increase the risk of complications with no demonstrated clinical

Principal Component Analysis of Body Measurements In Three ...

African Journals Online (AJOL)

This study was conducted to explore the relationship among body measurements in 3 strains of broilers chicken (Arbor Acre, Marshal and Ross) using principal component analysis with the view of identifying those components that define body conformation in broilers. A total of 180 birds were used, 60 per strain.

Fault tree analysis with multistate components

International Nuclear Information System (INIS)

Caldarola, L.

1979-02-01

A general analytical theory has been developed which allows one to calculate the occurence probability of the top event of a fault tree with multistate (more than states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of Boolean algebra is required. This is called 'Boolean algebra with restrictions on varibales' and its basic rules are the same as those of the traditional Boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. Important features of the method are the identification of the complete base and of the smallest irredundant base of a Boolean function which does not necessarily need to be coherent. It is shown that the identification of the complete base of a Boolean function requires the application of some algorithms which are not used in today's computer programmes for fault tree analysis. The problem of statistical dependence among primary components is discussed. The paper includes a small demonstrative example to illustrate the method. The example includes also statistical dependent components. (orig.) [de

Multistage principal component analysis based method for abdominal ECG decomposition

International Nuclear Information System (INIS)

Petrolis, Robertas; Krisciukaitis, Algimantas; Gintautas, Vladas

2015-01-01

Reflection of fetal heart electrical activity is present in registered abdominal ECG signals. However this signal component has noticeably less energy than concurrent signals, especially maternal ECG. Therefore traditionally recommended independent component analysis, fails to separate these two ECG signals. Multistage principal component analysis (PCA) is proposed for step-by-step extraction of abdominal ECG signal components. Truncated representation and subsequent subtraction of cardio cycles of maternal ECG are the first steps. The energy of fetal ECG component then becomes comparable or even exceeds energy of other components in the remaining signal. Second stage PCA concentrates energy of the sought signal in one principal component assuring its maximal amplitude regardless to the orientation of the fetus in multilead recordings. Third stage PCA is performed on signal excerpts representing detected fetal heart beats in aim to perform their truncated representation reconstructing their shape for further analysis. The algorithm was tested with PhysioNet Challenge 2013 signals and signals recorded in the Department of Obstetrics and Gynecology, Lithuanian University of Health Sciences. Results of our method in PhysioNet Challenge 2013 on open data set were: average score: 341.503 bpm 2 and 32.81 ms. (paper)

Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods

NARCIS (Netherlands)

Saccenti, E.; Camacho, J.

2015-01-01

Principal component analysis is one of the most commonly used multivariate tools to describe and summarize data. Determining the optimal number of components in a principal component model is a fundamental problem in many fields of application. In this paper we compare the performance of several

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

Science.gov (United States)

LaViolette, Peter S; Daun, Mitchell K; Paulson, Eric S; Schmainda, Kathleen M

2014-02-01

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.

Importance Analysis of In-Service Testing Components for Ulchin Unit 3

International Nuclear Information System (INIS)

Dae-Il Kan; Kil-Yoo Kim; Jae-Joo Ha

2002-01-01

We performed an importance analysis of In-Service Testing (IST) components for Ulchin Unit 3 using the integrated evaluation method for categorizing component safety significance developed in this study. The importance analysis using the developed method is initiated by ranking the component importance using quantitative PSA information. The importance analysis of the IST components not modeled in the PSA is performed through the engineering judgment, based on the expertise of PSA, and the quantitative and qualitative information for the IST components. The PSA scope for importance analysis includes not only Level 1 and 2 internal PSA but also Level 1 external and shutdown/low power operation PSA. The importance analysis results of valves show that 167 (26.55%) of the 629 IST valves are HSSCs and 462 (73.45%) are LSSCs. Those of pumps also show that 28 (70%) of the 40 IST pumps are HSSCs and 12 (30%) are LSSCs. (authors)

Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

Directory of Open Access Journals (Sweden)

R. Youmaran

2012-01-01

Full Text Available This paper develops an approach to measure the information content in a biometric feature representation of iris images. In this context, the biometric feature information is calculated using the relative entropy between the intraclass and interclass feature distributions. The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates processed using Principal-Component Analysis- (PCA- and Independent-Component Analysis- (ICA- based feature decomposition schemes. From this, the biometric feature information is calculated to be approximately 278 bits for PCA and 288 bits for ICA iris features using Masek's iris recognition scheme. This value approximately matches previous estimates of iris information content.

Blind speech separation system for humanoid robot with FastICA for audio filtering and separation

Science.gov (United States)

Budiharto, Widodo; Santoso Gunawan, Alexander Agung

2016-07-01

Nowadays, there are many developments in building intelligent humanoid robot, mainly in order to handle voice and image. In this research, we propose blind speech separation system using FastICA for audio filtering and separation that can be used in education or entertainment. Our main problem is to separate the multi speech sources and also to filter irrelevant noises. After speech separation step, the results will be integrated with our previous speech and face recognition system which is based on Bioloid GP robot and Raspberry Pi 2 as controller. The experimental results show the accuracy of our blind speech separation system is about 88% in command and query recognition cases.

Conglomerado de casos de tuberculosis multidrogorresistente en un colegio del distrito de Ica, Perú Cluster of multidrug-resistant tuberculosis cases in a school of the district of Ica, Perú

Directory of Open Access Journals (Sweden)

Julio Torres

2011-09-01

Full Text Available Se describe la evolución y las características de los casos de un conglomerado de tuberculosis multidrogorresistente (MDR ocurrido el año 2001, en un centro educativo localizado en una zona urbano marginal del distrito de Ica, Perú. Se identificó 15 escolares que estuvieron relacionados entre ellos antes de enfermar de tuberculosis. El promedio de edad fue 15 años. Doce casos fueron MDR y siete fueron resistentes a las cinco drogas de primera línea (RHEZS, cinco de los casos recibieron tres diferentes esquemas de tratamiento antituberculoso; el tiempo promedio de tratamiento antituberculoso fue de 37 meses (mínimo 21 y máximo 59 meses. Trece casos curaron y dos fallecieron. El presente estudio documenta un conglomerado de casos de TB-MDR en un centro educativo que, por los vínculos epidemiológicos y la simultaneidad en que aparecieron, podría tratarse de un probable brote de TB-MDR, con un desenlace satisfactorio luego de un tratamiento prolongado.We describe the evolution and features of a cluster of Multidrug-resistant tuberculosis (MDR TB cases that occurred in 2001, in a school located in a sub-urban area of the district of Ica, Peru. We identified 15 students related before becoming infected with tuberculosis. The mean age of the cluster was 15 years. A total of 12 students were MDR-TB cases and 7 were drug-resistant to 5 first-line drugs (RHEZS. Five out of the 15 cases received at least 3 different anti-tuberculosis treatment schemes. The average treatment duration was 37 months (minimum 21 and maximum 59 months. A total of 13 cases recovered and 2 died. This study describes a cluster of MDR -TB cases in an educational facility, which due to the epidemiological link and time presentation, is probably an outbreak of MDR TB with a satisfactory outcome after prolonged treatment.

Final report on the reactor pressure vessel pressurized-thermal-shock. International comparative assessment study (RPV PTS ICAS)

International Nuclear Information System (INIS)

Sievers, J.; Schulz, H.; Bass, R.; Pugh, C.

1999-10-01

A summary of the recently completed International Comparative Assessment Study of Pressurized-Thermal-Shock in Reactor Pressure Vessels (RPV PTS ICAS) is presented here to record the results in actual and comparative fashions. Within the DFM task, where account was taken of material properties and boundary conditions, reasonable agreement was obtained in linear-elastic and elastic-plastic analysis results. Linear elastic analyses and J-estimation schemes were shown to provide conservative estimates of peak crack driving force when compared with those obtained using complex three-dimensional (3D) finite element analyses. Predictions of RT NDT generally showed less scatter than that observed in crack driving force calculations due to the fracture toughness curve used for fracture assessment in the transition temperature region. Observed scatter in some analytical results could be traced mainly to a misinterpretation of the thermal expansion coefficient data given for the cladding and base metal. Also, differences in some results could be due to a quality assurance problem related to procedures for approximating the loading data given in the Problem Statement. For the PFM task, linear-elastic solutions were again shown to be conservative with respect to elastic-plastic solutions (by a factor of 2 to 4). Scatter in solutions obtained using the same computer code was generally attributable to differences in input parameters, e.g. standard deviations for the initial value of RT NDT , as well as for nickel and copper content. In the THM task, while there was a high degree of scatter during the early part of the transient, reasonable agreement in results was obtained during the latter part of the transient. Generally, the scatter was due to differences in analytical approaches used by participants, which included correlation-based engineering methods, system codes and three-dimensional computational fluids dynamics codes. Some of the models used to simulate condensation

Group-wise Principal Component Analysis for Exploratory Data Analysis

NARCIS (Netherlands)

Camacho, J.; Rodriquez-Gomez, Rafael A.; Saccenti, E.

2017-01-01

In this paper, we propose a new framework for matrix factorization based on Principal Component Analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new

FONTES PROTÉICAS DE ORIGEM VEGETAL EM DIETAS PARA JUVENIS DE CARPA HÚNGARA (Cyprinus carpio)

OpenAIRE

Giovani Taffarel Bergamin

2009-01-01

O objetivo deste trabalho foi avaliar a resposta de diferentes fontes protéicas de origem vegetal em substituição à farinha de carne suína da dieta, sobre o crescimento, rendimento e qualidade de carcaça, parâmetros sangüíneos e avaliação sensorial dos filés de juvenis de carpa húngara. Foram realizados dois experimentos, utilizando sistema de recirculação de água com 15 unidades experimentais (280L). No experimento 1, foram avaliadas cinco dietas experimentais. Uma dieta controle (FCS) co...

ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.

Science.gov (United States)

Mognon, Andrea; Jovicich, Jorge; Bruzzone, Lorenzo; Buiatti, Marco

2011-02-01

A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Copyright © 2010 Society for Psychophysiological Research.

Real Time Engineering Analysis Based on a Generative Component Implementation

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Klitgaard, Jens

2007-01-01

The present paper outlines the idea of a conceptual design tool with real time engineering analysis which can be used in the early conceptual design phase. The tool is based on a parametric approach using Generative Components with embedded structural analysis. Each of these components uses the g...

Abstract Interfaces for Data Analysis Component Architecture for Data Analysis Tools

CERN Document Server

Barrand, G; Dönszelmann, M; Johnson, A; Pfeiffer, A

2001-01-01

The fast turnover of software technologies, in particular in the domain of interactivity (covering user interface and visualisation), makes it difficult for a small group of people to produce complete and polished software-tools before the underlying technologies make them obsolete. At the HepVis '99 workshop, a working group has been formed to improve the production of software tools for data analysis in HENP. Beside promoting a distributed development organisation, one goal of the group is to systematically design a set of abstract interfaces based on using modern OO analysis and OO design techniques. An initial domain analysis has come up with several categories (components) found in typical data analysis tools: Histograms, Ntuples, Functions, Vectors, Fitter, Plotter, Analyzer and Controller. Special emphasis was put on reducing the couplings between the categories to a minimum, thus optimising re-use and maintainability of any component individually. The interfaces have been defined in Java and C++ and i...

Component Analysis of Long-Lag, Wide-Pulse Gamma-Ray Burst ...

Indian Academy of Sciences (India)

Principal Component Analysis of Long-Lag, Wide-Pulse Gamma-Ray. Burst Data. Zhao-Yang Peng. ∗. & Wen-Shuai Liu. Department of Physics, Yunnan Normal University, Kunming 650500, China. ∗ e-mail: pzy@ynao.ac.cn. Abstract. We have carried out a Principal Component Analysis (PCA) of the temporal and spectral ...

Age-related alterations of brain network underlying the retrieval of emotional autobiographical memories: an fMRI study using independent component analysis.

Science.gov (United States)

Ge, Ruiyang; Fu, Yan; Wang, Dahua; Yao, Li; Long, Zhiying

2014-01-01

Normal aging has been shown to modulate the neural underpinnings of autobiographical memory and emotion processing. Moreover, previous researches have suggested that aging produces a "positivity effect" in autobiographical memory. Although a few imaging studies have investigated the neural mechanism of the positivity effect, the neural substrates underlying the positivity effect in emotional autobiographical memory is unclear. To understand the age-related neural changes in emotional autobiographical memory that underlie the positivity effect, the present functional magnetic resonance imaging (fMRI) study used the independent component analysis (ICA) method to compare brain networks in younger and older adults as they retrieved positive and negative autobiographical events. Compared to their younger counterparts, older adults reported relatively higher positive feelings when retrieving emotional autobiographical events. Imaging data indicated an age-related reversal within the ventromedial prefrontal/anterior cingulate cortex (VMPFC/ACC) and the left amygdala of the brain networks that were engaged in the retrieval of autobiographical events with different valence. The retrieval of negative events compared to positive events induced stronger activity in the VMPFC/ACC and weaker activity in the amygdala for the older adults, whereas the younger adults showed a reversed pattern. Moreover, activity in the VMPFC/ACC within the task-related networks showed a negative correlation with the emotional valence intensity. These results may suggest that the positivity effect in older adults' autobiographical memories is potentially due to age-related changes in controlled emotional processing implemented by the VMPFC/ACC-amygdala circuit.

Age-related alterations of brain network underlying the retrieval of emotional autobiographical memories: An fMRI study using independent component analysis

Directory of Open Access Journals (Sweden)

Ruiyang eGe

2014-08-01

Full Text Available Normal aging has been shown to modulate the neural underpinnings of autobiographical memory and emotion processing. Moreover, previous researches have suggested that aging produces a positivity effect in autobiographical memory. Although a few imaging studies have investigated the neural mechanism of the positivity effect, the neural substrates underlying the positivity effect in emotional autobiographical memory is unclear. To understand the age-related neural changes in emotional autobiographical memory that underlie the positivity effect, the present functional magnetic resonance imaging (fMRI study used the independent component analysis (ICA method to compare brain networks in younger and older adults as they retrieved positive and negative autobiographical events. Compared to their younger counterparts, older adults reported relatively higher positive feelings when retrieving emotional autobiographical events. Imaging data indicated an age-related reversal within the ventromedial prefrontal/anterior cingulate cortex (VMPFC/ACC and the left amygdala of the brain networks that were engaged in the retrieval of autobiographical events with different valence. The retrieval of negative events compared to positive events induced stronger activity in the VMPFC/ACC and weaker activity in the amygdala for the older adults, whereas the younger adults showed a reversed pattern. Moreover, activity in the VMPFC/ACC within the task-related networks showed a negative correlation with the emotional valence intensity. These results may suggest that the positivity effect in older adults’ autobiographical memories is potentially due to age-related changes in controlled emotional processing implemented by the VMPFC/ACC-amygdala circuit.

Use of Sparse Principal Component Analysis (SPCA) for Fault Detection

DEFF Research Database (Denmark)

Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet

2016-01-01

Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

Maadooliat, Mehdi; Huang, Jianhua Z.; Hu, Jianhua

2015-01-01

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior

NEPR Principle Component Analysis - NOAA TIFF Image

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This GeoTiff is a representation of seafloor topography in Northeast Puerto Rico derived from a bathymetry model with a principle component analysis (PCA). The area...

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

Energy Technology Data Exchange (ETDEWEB)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng [Jiangnan University, Wuxi (China)

2014-11-15

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

International Nuclear Information System (INIS)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

2014-01-01

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

A new method for chaos control in communication systems

International Nuclear Information System (INIS)

Lin, S.-L.; Tung, P.-C.

2009-01-01

With the increasing needs of global communication, the improvement of secure communication is of vital importance. This study proposes a new scheme for establishing secure communication systems. The new scheme separates white Gaussian noises from the chaotic signals with modified Independent Component Analysis (ICA) and then controls each chaotic signal. This scheme is able to deal with white Gaussian noises in the natural world. However, the signals separated by traditional ICA shows opposite phase and unequal amplitude, making chaos control impossible. Our study proposed a modified ICA, which can calculate accurately the phase and amplitude and ensure control of the chaotic systems. The result indicates that our proposed system can successfully separate white Gaussian noise and stabilize all the chaotic signals.

Independent component analysis for automatic note extraction from musical trills

Science.gov (United States)

Brown, Judith C.; Smaragdis, Paris

2004-05-01

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

PCA: Principal Component Analysis for spectra modeling

Science.gov (United States)

Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

2012-07-01

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

BUSINESS PROCESS MANAGEMENT SYSTEMS TECHNOLOGY COMPONENTS ANALYSIS

Directory of Open Access Journals (Sweden)

Andrea Giovanni Spelta

2007-05-01

Full Text Available The information technology that supports the implementation of the business process management appproach is called Business Process Management System (BPMS. The main components of the BPMS solution framework are process definition repository, process instances repository, transaction manager, conectors framework, process engine and middleware. In this paper we define and characterize the role and importance of the components of BPMS's framework. The research method adopted was the case study, through the analysis of the implementation of the BPMS solution in an insurance company called Chubb do Brasil. In the case study, the process "Manage Coinsured Events"" is described and characterized, as well as the components of the BPMS solution adopted and implemented by Chubb do Brasil for managing this process.

Efeito da dieta hiperlipídico-protéica no metabolismo de ratos wistar adultos

OpenAIRE

Borba, Alberto José de

2008-01-01

O consumo de dietas com pouco carboidrato e rica em proteínas e gorduras faz parte do rol de dietas usadas para a perda de peso. A princípio, essas dietas levam à mobilização de triglicerídeos para a produção de corpos cetônicos e manutenção das funções corporais. O presente trabalho testou o efeito de uma dieta hiperlipídico-protéica (HLP) com baixo teor de carboidrato sobre o peso corporal, peso dos órgãos, consumo de ração, parâmetros bioquímicos séricos e alterações histopatológicas...

Efficacy of the Principal Components Analysis Techniques Using ...

African Journals Online (AJOL)

Second, the paper reports results of principal components analysis after the artificial data were submitted to three commonly used procedures; scree plot, Kaiser rule, and modified Horn's parallel analysis, and demonstrate the pedagogical utility of using artificial data in teaching advanced quantitative concepts. The results ...

Functional Generalized Structured Component Analysis.

Science.gov (United States)

Suk, Hye Won; Hwang, Heungsun

2016-12-01

An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.

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

Directory of Open Access Journals (Sweden)

Stefania Salvatore

2016-07-01

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

Integration of independent component analysis with near-infrared spectroscopy for analysis of bioactive components in the medicinal plant Gentiana scabra Bunge

Directory of Open Access Journals (Sweden)

Yung-Kun Chuang

2014-09-01

Full Text Available Independent component (IC analysis was applied to near-infrared spectroscopy for analysis of gentiopicroside and swertiamarin; the two bioactive components of Gentiana scabra Bunge. ICs that are highly correlated with the two bioactive components were selected for the analysis of tissue cultures, shoots and roots, which were found to distribute in three different positions within the domain [two-dimensional (2D and 3D] constructed by the ICs. This setup could be used for quantitative determination of respective contents of gentiopicroside and swertiamarin within the plants. For gentiopicroside, the spectral calibration model based on the second derivative spectra produced the best effect in the wavelength ranges of 600–700 nm, 1600–1700 nm, and 2000–2300 nm (correlation coefficient of calibration = 0.847, standard error of calibration = 0.865%, and standard error of validation = 0.909%. For swertiamarin, a spectral calibration model based on the first derivative spectra produced the best effect in the wavelength ranges of 600–800 nm and 2200–2300 nm (correlation coefficient of calibration = 0.948, standard error of calibration = 0.168%, and standard error of validation = 0.216%. Both models showed a satisfactory predictability. This study successfully established qualitative and quantitative correlations for gentiopicroside and swertiamarin with near-infrared spectra, enabling rapid and accurate inspection on the bioactive components of G. scabra Bunge at different growth stages.

Tomato sorting using independent component analysis on spectral images

NARCIS (Netherlands)

Polder, G.; Heijden, van der G.W.A.M.; Young, I.T.

2003-01-01

Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components

Proteins Play Important Role in Intercellular Adhesion Affecting on Fruit Textural Quality

DEFF Research Database (Denmark)

Bahadur Adhikari, Khem; Shomer, Ilan

2012-01-01

Fruit textural quality is becoming a major quality parameter for export, postharvest preservation, handling and processing. The main determinant of textural quality is intercellular adhesion (ICA) as attributed by the cell wall (CW) and its components. The importance of CW protein in ICA strength......Fruit textural quality is becoming a major quality parameter for export, postharvest preservation, handling and processing. The main determinant of textural quality is intercellular adhesion (ICA) as attributed by the cell wall (CW) and its components. The importance of CW protein in ICA...... strengthening was exempli ed in Medjoul date (Phoenix dactylifera L.) fruit, as a model. Fruit mesocarp sensitively responded to culture environment which was assayed in vitro at pH 3.5( pKa) in presence of organic acid molecules. The max penetration force, as a measure of ICA strength, of p...

Formación de mazorcas en diferentes nudos del eje de maíz ICA -V-51O

Directory of Open Access Journals (Sweden)

Laverde Peña Héctor

1986-12-01

Full Text Available Con el objeto de estudiar la capacidad biológica de producción de mazorca en diferentes nudos de la planta de maiz ICA -V-51O y determinar la mejora en producción de granos, cuando se efectúa polinización manual, se realizó un ensayo en el Centro Experimental de Tibaitatá en el ano de 1979. Para tal efecto se efectuaron decapitaciones a partir del entrenudo uno hasta el décimo y las mazorcas que fueron apareciendo en los diferentes nudos por efecto "del tratamiento se polinizaban manualmente. Los resultados obtenidos indican que existe capacidad biológica para el crecimiento y desarrollo de mazorcas en nudos donde usualmente no se presentan y que su tamaño y grado de acumulación de materia seca difieren en alta proporción de las normales.The biological capacity to produce ears In different nodes on corn plants of the variety ICA V-510 and the improvement in grain production with hand polinization was studied at the Tibaitatá, Agricultural Experimental Station. Decapitation internode were done from the flrst to the tenth node and the ears that come out on the nodes were pollinated by hand. The results showed a biological capacity of the plants to grow ears on nodes where normally they do not grow. Size and accumulation of dry matter are very different from normal ears.

Statistical Signal Processing in Humanitarian Mine Clerance Systems

DEFF Research Database (Denmark)

Karlsen, Brian; Sørensen, Helge Bjarup Dissing; Larsen, Jan

2002-01-01

Denne artikel beskriver kortfattet metoder og resultater relateret til clutterreduktion (clutter: uønskede reflekterede signaler) i jordradar- (eng. ground penetrating radar, GPR) signaler vha. statistiske signalbehandlingsmetoder baseret på Independent Component Analysis (ICA). Formålet ved denne...

Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis

Directory of Open Access Journals (Sweden)

CHEN, R.

2017-11-01

Full Text Available In this paper, based on Kernel Principal Component Analysis (KPCA of Phasor Measurement Units (PMU data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.

A review of multivariate analyses in imaging genetics

Directory of Open Access Journals (Sweden)

Jingyu eLiu

2014-03-01

Full Text Available Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a prior driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA, and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and

Experimental modal analysis of components of the LHC experiments

CERN Document Server

Guinchard, M; Catinaccio, A; Kershaw, K; Onnela, A

2007-01-01

Experimental modal analysis of components of the LHC experiments is performed with the purpose of determining their fundamental frequencies, their damping and the mode shapes of light and fragile detector components. This process permits to confirm or replace Finite Element analysis in the case of complex structures (with cables and substructure coupling). It helps solving structural mechanical problems to improve the operational stability and determine the acceleration specifications for transport operations. This paper describes the hardware and software equipment used to perform a modal analysis on particular structures such as a particle detector and the method of curve fitting to extract the results of the measurements. This paper exposes also the main results obtained for the LHC Experiments.

Determinação da solubilidade protéica de farinhas de subproduto de aves com a pepsina em baixa concentração Determination of solubility of poultry by-product with pepsin in low concentration

Directory of Open Access Journals (Sweden)

Claudio Bellaver

2004-10-01

Full Text Available Foi proposto recentemente que a solubilidade protéica in vitro com pepsina na concentração de 0,0002% é melhor que em concentrações maiores, para melhor classificar a qualidade protéica de farinhas de origem animal (FOA. Entretanto, nessa concentração, desconhece-se qual é o intervalo que melhor exprime a qualidade por meio da solubilidade protéica das FOA. Este trabalho foi conduzido com o objetivo de identificar os valores da solubilidade protéica de farinhas de subprodutos do abate de aves (FSA, utilizadas como modelo experimental. As fontes protéicas foram duas FSA e caseína (considerada padrão com 100% de solubilidade protéica. Todas as farinhas foram autoclavadas a 120ºC, durante 0, 10, 20, 40 e 80 minutos. As concentrações utilizadas da pepsina em solução de HCl 0,0744N foram de 0,0002 e 0,02%. O delineamento experimental foi o de blocos casualizados em esquema fatorial 3 (fontes x 5 (temperaturas x 2 (concentrações, com quatro repetições para a enzima na concentração de 0,0002% e duas repetições para a concentração de 0,02%. Foram determinados os valores de solubilidade protéica in vitro em solução ácido/pepsina e energia metabolizável in vivo das FSA. Os resultados indicaram que, na concentração de pepsina de 0,0002%, os valores de boa qualidade das FSA estão acima de 55%. Da mesma forma, solubilidades próximas a 84% podem ser consideradas boas, mas decaem em solubilidade e são consideradas ruins se tiverem em torno de 67% de solubilidade protéica em pepsina a 0,02%.It was recently proposed that the in vitro protein solubility is better to classify animal by-product meals (ABP using pepsin concentration of .0002% than with higher concentrations. However on this concentration no reference value exists to express ABP quality based on protein solubility. The objective of this study was to obtain protein solubility values for poultry by-product meals (PBP, as model to ABP. Two PBP and casein

Cerebral hemodynamic changes measured by gradient-echo or spin-echo bolus tracking and its correlation to changes in ICA blood flow measured by phase-mapping MRI

DEFF Research Database (Denmark)

Marstrand, J.R.; Rostrup, Egill; Garde, Ellen

2001-01-01

Changes in cerebral blood flow (CBF) induced by Acetazolamide (ACZ) were measured using dynamic susceptibility contrast MRI (DSC-MRI) with both spin echo (SE) EPI and gradient echo (GE) EPI, and related to changes in internal carotid artery (ICA) flow measured by phase-mapping. Also examined...... was the effect of repeated bolus injections. CBF, cerebral blood volume (CBV), and mean transit time (MTT) were calculated by singular value decomposition (SVD) and by deconvolution using an exponential function as kernel. The results showed no dependency on calculation method. GE-EPI measured a significant...... increase in CBF and CBV in response to ACZ, while SE-EPI measured a significant increase in CBV and MTT. CBV and MTT change measured by SE-EPI was sensitive to previous bolus injections. There was a significant linear relation between change in CBF measured by GE-EPI and change in ICA flow. In conclusion...

PEMBUATAN PERANGKAT LUNAK PENGENALAN WAJAH MENGGUNAKAN PRINCIPAL COMPONENTS ANALYSIS

Directory of Open Access Journals (Sweden)

Kartika Gunadi

2001-01-01

Full Text Available Face recognition is one of many important researches, and today, many applications have implemented it. Through development of techniques like Principal Components Analysis (PCA, computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched. Principal Components Analysis was used to reduce facial image dimension into fewer variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification Abstract in Bahasa Indonesia : Pengenalan wajah manusia merupakan salah satu bidang penelitian yang penting, dan dewasa ini banyak aplikasi yang dapat menerapkannya. Melalui pengembangan suatu teknik seperti Principal Components Analysis (PCA, komputer sekarang dapat melebihi kemampuan otak manusia dalam berbagai tugas pengenalan wajah, terutama tugas-tugas yang membutuhkan pencarian pada database wajah yang besar. Principal Components Analysis digunakan untuk mereduksi dimensi gambar wajah sehingga menghasilkan variabel yang lebih sedikit yang lebih mudah untuk diobsevasi dan ditangani. Hasil yang diperoleh kemudian akan dimasukkan ke suatu jaringan saraf tiruan dengan metode Backpropagation untuk mengenali gambar wajah yang telah diinputkan ke dalam sistem. Hasil pengujian sistem menunjukkan bahwa penggunaan PCA untuk pengenalan wajah dapat memberikan tingkat akurasi yang cukup tinggi. Untuk gambar wajah yang diikutsertakankan dalam latihan, dapat diperoleh 100% identifikasi yang benar. Dari beberapa kombinasi jaringan yang

Oil classification using X-ray scattering and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T., E-mail: dani.almeida84@gmail.com, E-mail: ricardo@lin.ufrj.br, E-mail: amandass@bioqmed.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil); Oliveira, Davi F.; Anjos, Marcelino J., E-mail: davi.oliveira@uerj.br, E-mail: marcelin@uerj.br [Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ (Brazil). Inst. de Fisica Armando Dias Tavares

2015-07-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

Oil classification using X-ray scattering and principal component analysis

International Nuclear Information System (INIS)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T.; Oliveira, Davi F.; Anjos, Marcelino J.