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Sample records for component analysis ica

  1. Unsupervised hyperspectral image analysis using independent component analysis (ICA)

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    S. S. Chiang; I. W. Ginsberg

    2000-06-30

    In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.

  2. An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

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    Dominique Gosselin

    2010-03-01

    Full Text Available This paper presents an introduction to independent component analysis (ICA. Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax with the use of the Mathematica software.

  3. INDEPENDENT COMPONENT ANALYSIS (ICA) APPLIED TO LONG BUNCH BEAMS IN THE LOS ALAMOS PROTON STORAGE RING

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    Kolski, Jeffrey S. [Los Alamos National Laboratory; Macek, Robert J. [Los Alamos National Laboratory; McCrady, Rodney C. [Los Alamos National Laboratory; Pang, Xiaoying [Los Alamos National Laboratory

    2012-05-14

    Independent component analysis (ICA) is a powerful blind source separation (BSS) method. Compared to the typical BSS method, principal component analysis (PCA), which is the BSS foundation of the well known model independent analysis (MIA), ICA is more robust to noise, coupling, and nonlinearity. ICA of turn-by-turn beam position data 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 and discuss the source signals identified as betatron motion and longitudinal beam structure.

  4. A Stock Market Prediction Method Based on Support Vector Machines (SVM and Independent Component Analysis (ICA

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    Hakob GRIGORYAN

    2016-08-01

    Full Text Available The research presented in this work focuses on financial time series prediction problem. The integrated prediction model based on support vector machines (SVM with independent component analysis (ICA (called SVM-ICA is proposed for stock market prediction. The presented approach first uses ICA technique to extract important features from the research data, and then applies SVM technique to perform time series prediction. The results obtained from the SVM-ICA technique are compared with the results of SVM-based model without using any pre-processing step. In order to show the effectiveness of the proposed methodology, two different research data are used as illustrative examples. In experiments, the root mean square error (RMSE measure is used to evaluate the performance of proposed models. The comparative analysis leads to the conclusion that the proposed SVM-ICA model outperforms the simple SVM-based model in forecasting task of nonstationary time series.

  5. Task-evoked brain functional magnetic susceptibility mapping by independent component analysisICA).

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    Chen, Zikuan; Calhoun, Vince D

    2016-03-01

    Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. A robust independent component analysis (ICA) model for functional magnetic resonance imaging (fMRI) data

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    Ao, Jingqi; Mitra, Sunanda; Liu, Zheng; Nutter, Brian

    2011-03-01

    The coupling of carefully designed experiments with proper analysis of functional magnetic resonance imaging (fMRI) data provides us with a powerful as well as noninvasive tool to help us understand cognitive processes associated with specific brain regions and hence could be used to detect abnormalities induced by a diseased state. The hypothesisdriven General Linear Model (GLM) and the data-driven Independent Component Analysis (ICA) model are the two most commonly used models for fMRI data analysis. A hybrid ICA-GLM model combines the two models to take advantages of benefits from both models to achieve more accurate mapping of the stimulus-induced activated brain regions. We propose a modified hybrid ICA-GLM model with probabilistic ICA that includes a noise model. In this modified hybrid model, a probabilistic principle component analysis (PPCA)-based component number estimation is used in the ICA stage to extract the intrinsic number of original time courses. In addition, frequency matching is introduced into the time course selection stage, along with temporal correlation, F-test based model fitting estimation, and time course combination, to produce a more accurate design matrix for GLM. A standard fMRI dataset is used to compare the results of applying GLM and the proposed hybrid ICA-GLM in generating activation maps.

  7. Unsupervised component analysis: PCA, POA and ICA data exploring - connecting the dots

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    Pereira, Jorge Costa; Azevedo, Julio Cesar R.; Knapik, Heloise G.; Burrows, Hugh Douglas

    2016-08-01

    Under controlled conditions, each compound presents a specific spectral activity. Based on this assumption, this article discusses Principal Component Analysis (PCA), Principal Object Analysis (POA) and Independent Component Analysis (ICA) algorithms and some decision criteria in order to obtain unequivocal information on the number of active spectral components present in a certain aquatic system. The POA algorithm was shown to be a very robust unsupervised object-oriented exploratory data analysis, proven to be successful in correctly determining the number of independent components present in a given spectral dataset. In this work we found that POA combined with ICA is a robust and accurate unsupervised method to retrieve maximal spectral information (the number of components, respective signal sources and their contributions).

  8. Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-GARCH) models.

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    Wu, Edmond H C; Yu, Philip L H; Li, W K

    2006-10-01

    We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

  9. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

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    Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Maalouly, Jacqueline, E-mail: j_maalouly@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Rutledge, Douglas N., E-mail: douglas.rutledge@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Chebib, Hanna, E-mail: hchebib@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Ducruet, Violette, E-mail: violette.ducruet@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France)

    2014-11-15

    Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of

  10. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

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

    2014-11-01

    Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied

  11. Unified ICA-SPM analysis of fMRI experiments

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    Bjerre, Troels; Henriksen, Jonas; Nielsen, Carsten Haagen

    2009-01-01

    We present a toolbox for exploratory analysis of functional magnetic resonance imaging (fMRI) data using independent component analysis (ICA) within the widely used SPM analysis pipeline. The toolbox enables dimensional reduction using principal component analysis, ICA using several different ICA...... effects in fMRI data from a visual experiment....

  12. Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products.

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    Monakhova, Yulia B; Tsikin, Alexey M; Kuballa, Thomas; Lachenmeier, Dirk W; Mushtakova, Svetlana P

    2014-05-01

    The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non-negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple-to-use-interactive self-modeling mixture analysis (SIMPLISMA) and multivariate curve resolution-alternating least squares (MCR-ALS)] are applied for simultaneous (1)H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight-component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography-mass spectrometry as well as the MCR-ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures. Copyright © 2014 John Wiley & Sons, Ltd.

  13. Kombinasi Metode Independent Component Analysis (ICA dan Beamforming untuk Pemisahan Sinyal Akustik Bawah Air

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    Mandala Anugerahwan Firstanto

    2013-09-01

    Full Text Available Pada bidang perkapalan atau kelautan, sinyal akustik merupakan sebuah sinyal noise. Hal ini dikarenakan sinyal akustik yang ingin kita analisis tercampur dengan sinyal lain. Perlu adanya metode untuk memisahkan sinyal akustik dari suara yang terdapat dalam medium air dan mengetahui arah datang sumber suara. Dalam tugas akhir ini dilakukan pemisahan sinyal akustik dengan menggunakan kombinasi metode ICA dan beamforming. Pemisahan sinyal akustik dilakukan tiga kali. Pertama, simulasi pemisahan suara menggunakan pemodelan shallowwaterdengan algoritma FastICA dan delayandsum(DS beamformer. Kedua, dengan menggunakan toolboxICALAB. Ketiga, pemisahan sinyal akustik secara riil, hasil rekaman dengan menggunakan hydrophone. Hasil simulasi menunjukkan bahwa, algoritma FastICA dapat memisahkan sinyal akustik dengan baik. Hal ini ditunjukkan dengan nilai MSE sebesar 3.6399×〖10〗^(-5 dan nilai SIR sebesar 45,72dB. Pada pemisahan suara secara riil, jarak antara speaker dengan hydrophone menentukan kualitas pemisahan suara. Semakin jauh jarak antara speaker dengan hydrophone, semakin berkurang nilai MSE dan SIR pada proses pemisahan suara bawah air. Hal ini dapat dilihat pada jarak 1 meter nilai mean SIR bernilai 51,29dB, jarak 5 meter bernilai 48.55dB, jarak 10 meter bernilai 41,73dB. Algoritma DS beamformer dapat menentukan sumber suara dalam medium air dengan jarak minimal speaker dengan hydrophone10 meter. Peletakan speaker dan hydrophone yang saling berhadapan akan mendapatkan hasil yang maksimal.

  14. Attenuated total reflectance-mid infrared spectroscopy (ATR-MIR) coupled with independent components analysis (ICA): A fast method to determine plasticizers in polylactide (PLA).

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    Kassouf, Amine; Ruellan, Alexandre; Jouan-Rimbaud Bouveresse, Delphine; Rutledge, Douglas N; Domenek, Sandra; Maalouly, Jacqueline; Chebib, Hanna; Ducruet, Violette

    2016-01-15

    Compliance of plastic food contact materials (FCMs) with regulatory specifications in force, requires a better knowledge of their interaction phenomena with food or food simulants in contact. However these migration tests could be very complex, expensive and time-consuming. Therefore, alternative procedures were introduced based on the determination of potential migrants in the initial material, allowing the use of mathematical modeling, worst case scenarios and other alternative approaches, for simple and fast compliance testing. In this work, polylactide (PLA), plasticized with four different plasticizers, was considered as a model plastic formulation. An innovative analytical approach was developed, based on the extraction of qualitative and quantitative information from attenuated total reflectance (ATR) mid-infrared (MIR) spectral fingerprints, using independent components analysis (ICA). Two novel chemometric methods, Random_ICA and ICA_corr_y, were used to determine the optimal number of independent components (ICs). Both qualitative and quantitative information, related to the identity and the quantity of plasticizers in PLA, were retrieved through a direct and fast analytical method, without any prior sample preparations. Through a single qualitative model with 11 ICs, a clear and clean classification of PLA samples was obtained, according to the identity of plasticizers incorporated in their formulations. Moreover, a quantitative model was established for each formulation, correlating proportions estimated by ICA and known concentrations of plasticizers in PLA. High coefficients of determination (higher than 0.96) and recoveries (higher than 95%) proved the good predictability of the proposed models.

  15. A Comparison of Independent Component Analysis (ICA) of fMRI and Electrical Source Imaging (ESI) in Focal Epilepsy Reveals Misclassification Using a Classifier.

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    Maziero, Danilo; Sturzbecher, Marcio; Velasco, Tonicarlo Rodrigues; Rondinoni, Carlo; Castellanos, Agustin Lage; Carmichael, David William; Salmon, Carlos Ernesto Garrido

    2015-11-01

    Interictal epileptiform discharges (IEDs) can produce haemodynamic responses that can be detected by electroencephalography-functional magnetic resonance imaging (EEG-fMRI) using different analysis methods such as the general linear model (GLM) of IEDs or independent component analysis (ICA). The IEDs can also be mapped by electrical source imaging (ESI) which has been demonstrated to be useful in presurgical evaluation in a high proportion of cases with focal IEDs. ICA advantageously does not require IEDs or a model of haemodynamic responses but its use in EEG-fMRI of epilepsy has been limited by its ability to separate and select epileptic components. Here, we evaluated the performance of a classifier that aims to filter all non-BOLD responses and we compared the spatial and temporal features of the selected independent components (ICs). The components selected by the classifier were compared to those components selected by a strong spatial correlation with ESI maps of IED sources. Both sets of ICs were subsequently compared to a temporal model derived from the convolution of the IEDs (derived from the simultaneously acquired EEG) with a standard haemodynamic response. Selected ICs were compared to the patients' clinical information in 13 patients with focal epilepsy. We found that the misclassified ICs clearly related to IED in 16/25 cases. We also found that the classifier failed predominantly due to the increased spectral range of fMRIs temporal responses to IEDs. In conclusion, we show that ICA can be an efficient approach to separate responses related to epilepsy but that contemporary classifiers need to be retrained for epilepsy data. Our findings indicate that, for ICA to contribute to the analysis of data without IEDs to improve its sensitivity, classification strategies based on data features other than IC time course frequency is required.

  16. 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.

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    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.

  17. Unified ICA-SPM analysis of fMRI experiments

    DEFF Research Database (Denmark)

    Bjerre, Troels; Henriksen, Jonas; Nielsen, Carsten Haagen

    2009-01-01

    algorithms, selection of the number of components using the Bayesian information criterion (BIC), visualization of ICA components, and extraction of components for subsequent analysis using the standard general linear model. We demonstrate how the toolbox is capable of identifying activity and nuisance...

  18. Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices

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    Timothy eMeier

    2012-10-01

    Full Text Available Parallel Independent Component Analysis (para-ICA is a multivariate method that can identify complex relationships between different data modalities by simultaneously performing Independent Component Analysis on each data set while finding mutual information between the two data sets. We use para-ICA to test the hypothesis that spatial sub-components of common resting state networks (RSNs covary with specific behavioral measures. Resting state scans and a battery of behavioral indices were collected from 24 younger adults. Group ICA was performed and common RSNs were identified by spatial correlation to publically available templates. Nine RSNs were identified and para-ICA was run on each network with a matrix of behavioral measures serving as the second data type. Five networks had spatial sub-components that significantly correlated with behavioral components. These included a sub-component of the temporo-parietal attention network that differentially covaried with different trial-types of a sustained attention task, sub-components of default mode networks that covaried with attention and working memory tasks, and a sub-component of the bilateral frontal network that split the left inferior frontal gyrus into three clusters according to its cytoarchitecture that differentially covaried with working memory performance. Additionally, we demonstrate the validity of para-ICA in cases with unbalanced dimensions using simulated data.

  19. CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis

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    Federico Raimondo

    2012-01-01

    Full Text Available In recent years, Independent Component Analysis (ICA has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation.

  20. CUDAICA: GPU optimization of Infomax-ICA EEG analysis.

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    Raimondo, Federico; Kamienkowski, Juan E; Sigman, Mariano; Fernandez Slezak, Diego

    2012-01-01

    In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation.

  1. Analysis of Fast- ICA Algorithm for Separation of Mixed Images

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    Tanmay Awasthy

    2013-10-01

    Full Text Available Independent component analysis (ICA is a newly developed method in which the aim is to find a linear representation of nongaussian statistics so that the components are statistically independent, or as independent as possible. Such techniques are actively being used in study of both statistical image processing and unsupervised neural learning application. This paper represents the Fast Independent component analysis algorithm for separation of mixed images. To solve the blind signal separation problems Independent component analysis approach used statistical independence of the source signals. This paper focuses on the theory and methods of ICA in contrast to classical transformations along with the applications of this method to blind source separation .For an illustration of the algorithm, visualized the immixing process with a set of images has been done. To express the results of our analysis simulations have been presented.

  2. Hand classification of fMRI ICA noise components.

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    Griffanti, Ludovica; Douaud, Gwenaëlle; Bijsterbosch, Janine; Evangelisti, Stefania; Alfaro-Almagro, Fidel; Glasser, Matthew F; Duff, Eugene P; Fitzgibbon, Sean; Westphal, Robert; Carone, Davide; Beckmann, Christian F; Smith, Stephen M

    2016-12-16

    We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

  3. Mean Field ICA

    DEFF Research Database (Denmark)

    Petersen, Kaare Brandt

    2006-01-01

    This thesis describes investigations and improvements of a technique for Independent Component Analysis (ICA), called "Mean Field ICA". The main focus of the thesis is the optimization part of the algorithm, the so-called "EM algorithm". Using different approaches it is demonstrated that the EM...... Gradient Recipe is applicable to a wide selection of models. Furthermore, the Mean Field ICA model is extended to incorporate ltering over time in a so-called "convolutive ICA" model. Finally, by using mixture of Gaussians as source priors, the generative and ltering approach to ICA is compared...

  4. Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

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    Winkler, Irene; Haufe, Stefan; Tangermann, Michael

    2011-08-02

    Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

  5. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

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    Tangermann Michael

    2011-08-01

    Full Text Available Abstract Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI or for Mental State Monitoring. While hand-optimized selection of source components derived from Independent Component Analysis (ICA to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM. The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT study, n = 12 that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP paradigm, n = 18; motor imagery BCI paradigm, n = 80 that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (10% Mean Squared Error (MSE on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of

  6. EEG artifact elimination by extraction of ICA-component features using image processing algorithms.

    Science.gov (United States)

    Radüntz, T; Scouten, J; Hochmuth, O; Meffert, B

    2015-03-30

    Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.

  7. Analysis and Processing of Magnato Encephato Graphy (MEG) Data Based on Independent Component Analysis (ICA)%基于独立元分析的MEG(脑磁图)数据分析和处理

    Institute of Scientific and Technical Information of China (English)

    王斌; 张立明

    2003-01-01

    独立元分析(independent component analysis,ICA)可用于分离混迭的MEG(Magnetoencephalography)多通道信号中的信号源.从ICA分解的结果中消除干扰源和噪声,并将剩余分量投影回MEG多通道数据空间,可得到净化的MEG信号,表示各个信号源的各独立元分别投影回多通道,可对各活动源进行空间定位.特别是,响应于外界刺激的诱发活动源亦可从重叠的MEG多通道信号中得到分离,这对脑功能研究及脑医学临床应用极有吸引力.提出了一个简单有效的基于ICA的MEG数据分析和处理方法,研究和分析了一些实际应用问题,特别是给出了听觉诱发响应的一些有意义的分析结果.

  8. 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....

  9. Consistent sparse representations of EEG ERP and ICA components based on wavelet and chirplet dictionaries.

    Science.gov (United States)

    Qiu, Jun-Wei; Zao, John K; Wang, Peng-Hua; Chou, Yu-Hsiang

    2010-01-01

    A randomized search algorithm for sparse representations of EEG event-related potentials (ERPs) and their statistically independent components is presented. This algorithm combines greedy matching pursuit (MP) technique with covariance matrix adaptation evolution strategy (CMA-ES) to select small number of signal atoms from over-complete wavelet and chirplet dictionaries that offer best approximations of quasi-sparse ERP signals. During the search process, adaptive pruning of signal parameters was used to eliminate redundant or degenerative atoms. As a result, the CMA-ES/MP algorithm is capable of producing accurate efficient and consistent sparse representations of ERP signals and their ICA components. This paper explains the working principles of the algorithm and presents the preliminary results of its use.

  10. 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...... semantics, not only in text, but also in dynamic text (chat), images, and combinations of text and images. Here we further expand on the relevance of the ICA model for representing context, including two new analyzes of abstract data: social networks and musical features....

  11. Advances in independent component analysis and learning machines

    CERN Document Server

    Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko

    2015-01-01

    In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

  12. Independent component analysis applications on THz sensing and imaging

    Science.gov (United States)

    Balci, Soner; Maleski, Alexander; Nascimento, Matheus Mello; Philip, Elizabath; Kim, Ju-Hyung; Kung, Patrick; Kim, Seongsin M.

    2016-05-01

    We report Independent Component Analysis (ICA) technique applied to THz spectroscopy and imaging to achieve a blind source separation. A reference water vapor absorption spectrum was extracted via ICA, then ICA was utilized on a THz spectroscopic image in order to clean the absorption of water molecules from each pixel. For this purpose, silica gel was chosen as the material of interest for its strong water absorption. The resulting image clearly showed that ICA effectively removed the water content in the detected signal allowing us to image the silica gel beads distinctively even though it was totally embedded in water before ICA was applied.

  13. Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling.

    Science.gov (United States)

    Monakhova, Yulia B; Mushtakova, Svetlana P

    2017-05-01

    A fast and reliable spectroscopic method for multicomponent quantitative analysis of targeted compounds with overlapping signals in complex mixtures has been established. The innovative analytical approach is based on the preliminary chemometric extraction of qualitative and quantitative information from UV-vis and IR spectral profiles of a calibration system using independent component analysis (ICA). Using this quantitative model and ICA resolution results of spectral profiling of "unknown" model mixtures, the absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated without reference solutions. Good recoveries generally between 95% and 105% were obtained. The method can be applied to any spectroscopic data that obey the Beer-Lambert-Bouguer law. The proposed method was tested on analysis of vitamins and caffeine in energy drinks and aromatic hydrocarbons in motor fuel with 10% error. The results demonstrated that the proposed method is a promising tool for rapid simultaneous multicomponent analysis in the case of spectral overlap and the absence/inaccessibility of reference materials.

  14. A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation

    Science.gov (United States)

    Götz, Th; Stadler, L.; Fraunhofer, G.; Tomé, A. M.; Hausner, H.; Lang, E. W.

    2017-02-01

    Objective. We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. Approach. EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. Main results. The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands. Significance. The proposed method provides various means to discriminate both disease pictures in a clinical environment.

  15. Facial Expression Recognition by Supervised Independent Component Analysis Using MAP Estimation

    Science.gov (United States)

    Chen, Fan; Kotani, Kazunori

    Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for de-mixing coefficients into the classical ICA to alleviate the problem. Since the prior is constructed upon the classification information from the training data, we refer to the proposed ICA model with a selective prior as a supervised ICA (sICA). We formulated the learning rule for sICA by taking a Maximum a Posteriori (MAP) scheme and further derived a fixed point algorithm for learning the de-mixing matrix. We investigate the performance of sICA in facial expression recognition from the aspects of both correct rate of recognition and robustness even with few independent components.

  16. Eliminate indeterminacies of independent component analysis for chemometrics

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An improved method has been proposed to eliminate the indeterminacies of independent component analysis (ICA) for chemomet- rics. Following the arrangement of principal components analysis (PCA), the ICA mixing matrix is selected as signal content indexes, and ICA output are sorted and directed. After many times reputations, independent components (Ics) are paired according to the maximum correlation coefficient, and then the mean values of each IC substitutes the original Ics. This indicates that the ICA inde- terminacies are eliminated. A simulation example is tested to validate this improvement. Finally, a set of experimental LC-MS data is processed without any prior knowledge or specific limitation and the results show that the improved ICA can directly separate the mixed signals in chemometrics, and it is simpler and more reasonable than the simple to use interactive self-modeling mixture analysis (SIMPLISMA).

  17. Independent Component Analysis in a convoluted world

    DEFF Research Database (Denmark)

    Dyrholm, Mads

    2006-01-01

    instantaneousICA, then select a physiologically interesting subspace, then remove the delayed temporal dependencies among the instantaneous ICA components by using convolutive ICA. By Bayesian model selection, in a real world EEG data set, it is shown that convolutive ICA is a better model for EEG than...

  18. Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Shanshan Li

    Full Text Available Functional magnetic resonance imaging (fMRI is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer's disease. In the first stage, spatial independent component analysis is applied to group fMRI data to obtain common brain networks (spatial maps and subject-specific mixing matrices (time courses. In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact logistic regression for matched pairs data. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.

  19. Source-space ICA for MEG source imaging

    Science.gov (United States)

    Jonmohamadi, Yaqub; Jones, Richard D.

    2016-02-01

    Objective. One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and localize multiple concurrent sources. Among inverse techniques, the minimum-variance beamformers offer a high spatial resolution. However, in order to have both high spatial resolution of beamformer and be able to take on multiple concurrent sources, sensor-space ICA + beamformer is not an ideal combination. Approach. We propose source-space ICA for MEG as a powerful alternative approach which can provide the high spatial resolution of the beamformer and handle multiple concurrent sources. The concept of source-space ICA for MEG is to apply the beamformer first and then singular value decomposition + ICA. In this paper we have compared source-space ICA with sensor-space ICA both in simulation and real MEG. The simulations included two challenging scenarios of correlated/concurrent cluster sources. Main Results. Source-space ICA provided superior performance in spatial reconstruction of source maps, even though both techniques performed equally from a temporal perspective. Real MEG from two healthy subjects with visual stimuli were also used to compare performance of sensor-space ICA and source-space ICA. We have also proposed a new variant of minimum-variance beamformer called weight-normalized linearly-constrained minimum-variance with orthonormal lead-field. Significance. As sensor-space ICA-based source reconstruction is popular in EEG and MEG imaging, and given that source-space ICA has superior spatial performance, it is expected that source-space ICA will supersede its predecessor in many applications.

  20. 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...... classification rates increase if based on multimedia components relative to single media analysis. For this purpose a simple probabilistic supervised classifier which works from unsupervised ICA features is invoked. In addition, we demonstrate the suggested framework for automatic annotation of descriptive key...

  1. Assessment of the capabilities of the temporal and spatiotemporal ICA method for geophysical signal separation in GRACE data

    National Research Council Canada - National Science Library

    Boergens, Eva; Rangelova, Elena; Sideris, Michael G; Kusche, Juergen

    2014-01-01

    We investigate the potential of two independent component analysis (ICA) methods, i.e., the temporal and spatiotemporal ICA, for separating geophysical signals in Gravity Recovery and Climate Experiment...

  2. Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xi; YAN Wei-wu; ZHAO Xu; SHAO Hui-he

    2007-01-01

    A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis (ICA) is proposed. The kernel ICA method is a two-phase algorithm: whitened kernel principal component (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

  3. gpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization

    Directory of Open Access Journals (Sweden)

    Nguyen Thang Viet

    2007-01-01

    Full Text Available A new geometric approach for nonlinear independent component analysis (ICA is presented in this paper. Nonlinear environment is modeled by the popular post nonlinear (PNL scheme. To eliminate the nonlinearity in the observed signals, a novel linearizing method named as geometric post nonlinear ICA (gpICA is introduced. Thereafter, a basic linear ICA is applied on these linearized signals to estimate the unknown sources. The proposed method is motivated by the fact that in a multidimensional space, a nonlinear mixture is represented by a nonlinear surface while a linear mixture is represented by a plane, a special form of the surface. Therefore, by geometrically transforming the surface representing a nonlinear mixture into a plane, the mixture can be linearized. Through simulations on different data sets, superior performance of gpICA algorithm has been shown with respect to other algorithms.

  4. gpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization

    Science.gov (United States)

    Nguyen, Thang Viet; Patra, Jagdish Chandra; Emmanuel, Sabu

    2006-12-01

    A new geometric approach for nonlinear independent component analysis (ICA) is presented in this paper. Nonlinear environment is modeled by the popular post nonlinear (PNL) scheme. To eliminate the nonlinearity in the observed signals, a novel linearizing method named as geometric post nonlinear ICA (gpICA) is introduced. Thereafter, a basic linear ICA is applied on these linearized signals to estimate the unknown sources. The proposed method is motivated by the fact that in a multidimensional space, a nonlinear mixture is represented by a nonlinear surface while a linear mixture is represented by a plane, a special form of the surface. Therefore, by geometrically transforming the surface representing a nonlinear mixture into a plane, the mixture can be linearized. Through simulations on different data sets, superior performance of gpICA algorithm has been shown with respect to other algorithms.

  5. Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis

    Directory of Open Access Journals (Sweden)

    Jianying Wang

    2016-01-01

    Full Text Available From the principle of independent component analysis (ICA and the uncertainty of amplitude, order, and number of source signals, this paper expounds the root reasons for modal energy uncertainty, identified order uncertainty, and modal missing in output-only modal analysis based on ICA methods. Aiming at the problem of lack of comparison and evaluation of different ICA algorithms for output-only modal analysis, this paper studies the different objective functions and optimization methods of ICA for output-only modal parameter identification. Simulation results on simply supported beam verify the effectiveness, robustness, and convergence rate of five different ICA algorithms for output-only modal parameters identification and show that maximization negentropy with quasi-Newton iterative of ICA method is more suitable for modal parameter identification.

  6. 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.

  7. 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.

  8. Comparison between SIMO-ICA with least squares criterion and SIMO-ICA with information-geometric learning

    OpenAIRE

    Tomoya Takatani; Tsuyoki Nishikawa; Hiroshi Saruwatari; Kiyohiro Shikano

    2004-01-01

    High-fidelity blind source separation (BSS) using Single-Input Multiple-Output (SIMO)-model-based Independent Component Analysis (SIMO-ICA) is now being studied by the authors. This paper describes a comparison of two types of SIMO-ICAs, SIMO-ICA-LS and SIMOICA-IG, with different constraints, and gives explicit discussion on the sensitivity of the parameters settings in the methods. In order to discuss the difference, the source separation experiments using two SIMO-ICAs are carried out under...

  9. Multiuser detection and independent component analysis-Progress and perspective

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The latest progress in the multiuser detection and independent component analysis (ICA) is reviewed systematically. Then two novel classes of multiuser detection methods based on ICA algorithms and feedforward neural networks are proposed. Theoretical analysis and computer simulation show that ICA algorithms are effective to detect multiuser signals in code-division multiple-access (CDMA) system. The performances of these methods are not identical entirely in various channels, but all of them are robust, efficient, fast and suitable for real-time implementations.

  10. Semi-blind independent component analysis of fMRI based on real-time fMRI system.

    Science.gov (United States)

    Ma, Xinyue; Zhang, Hang; Zhao, Xiaojie; Yao, Li; Long, Zhiying

    2013-05-01

    Real-time functional magnetic resonance imaging (fMRI) is a type of neurofeedback tool that enables researchers to train individuals to actively gain control over their brain activation. Independent component analysis (ICA) based on data-driven model is seldom used in real-time fMRI studies due to large time cost, though it has been very popular to offline analysis of fMRI data. The feasibility of performing real-time ICA (rtICA) processing has been demonstrated by previous study. However, rtICA was only applied to analyze single-slice data rather than full-brain data. In order to improve the performance of rtICA, we proposed semi-blind real-time ICA (sb-rtICA) for our real-time fMRI system by adding regularization of certain estimated time courses using the experiment paradigm information to rtICA. Both simulated and real-time fMRI experiment were conducted to compare the two approaches. Results from simulated and real full-brain fMRI data demonstrate that sb-rtICA outperforms rtICA in robustness, computational time and spatial detection power. Moreover, in contrast to rtICA, the first component estimated by sb-rtICA tends to be the target component in more sliding windows.

  11. 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....

  12. 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....

  13. A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

    Science.gov (United States)

    Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

    2016-08-01

    Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

  14. PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

    Science.gov (United States)

    Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

    2016-01-01

    Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals. PMID:27340397

  15. ICA Feature Extraction—Framework and Application

    Institute of Scientific and Technical Information of China (English)

    DINGPeilu

    2003-01-01

    Independent component analysls(ICA) has been recently used to flnd representation of Images with neuro-physlologlcal plausibility.This paper extends it to the problem of extracting intrinsic fature from variety of data.We first propose a general framework of ICA feature extraction and compare it with the well-known principal component analysis(PCA) method.Furthermore,we explore the application of proposed framework on biometric data-both face image and voice signals,to derive representations that are suitable for personal recognitio.Experiment shows that these features are superior to those commonly used features.

  16. Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts.

    Science.gov (United States)

    Pontifex, Matthew B; Gwizdala, Kathryn L; Parks, Andrew C; Billinger, Martin; Brunner, Clemens

    2017-03-01

    Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies.

  17. The two-component signal transduction system ArlRS regulates Staphylococcus epidermidis biofilm formation in an ica-dependent manner.

    Directory of Open Access Journals (Sweden)

    Yang Wu

    Full Text Available Due to its ability to form biofilms on medical devices, Staphylococcus epidermidis has emerged as a major pathogen of nosocomial infections. In this study, we investigated the role of the two-component signal transduction system ArlRS in regulating S. epidermidis biofilm formation. An ArlRS-deficient mutant, WW06, was constructed using S. epidermidis strain 1457 as a parental strain. Although the growth curve of WW06 was similar to that of SE1457, the mutant strain was unable to form biofilms in vitro. In a rabbit subcutaneous infection model, sterile disks made of polymeric materials were implanted subcutaneously followed with inoculation of WW06 or SE1457. The viable bacteria cells of WW06 recovered from biofilms on the embedded disks were much lower than that of SE1457. Complementation of arlRS genes expression from plasmid in WW06 restored biofilm-forming phenotype both in vivo and in vitro. WW06 maintained the ability to undergo initial attachment. Transcription levels of several genes involved in biofilm formation, including icaADBC, sigB, and sarA, were decreased in WW06, compared to SE1457; and icaR expression was increased in WW06, detected by real-time reverse-transcription PCR. The biofilm-forming phenotype was restored by overexpressing icaADBC in WW06 but not by overexpressing sigB, indicating that ArlRS regulates biofilm formation through the regulation of icaADBC. Gel shift assay showed that ArlR can bind to the promoter region of the ica operon. In conclusion, ArlRS regulates S. epidermidis biofilm formation in an ica-dependent manner, distinct from its role in S. aureus.

  18. The two-component signal transduction system ArlRS regulates Staphylococcus epidermidis biofilm formation in an ica-dependent manner.

    Science.gov (United States)

    Wu, Yang; Wang, Jiaxue; Xu, Tao; Liu, Jingran; Yu, Wenqi; Lou, Qiang; Zhu, Tao; He, Nianan; Ben, Haijing; Hu, Jian; Götz, Friedrich; Qu, Di

    2012-01-01

    Due to its ability to form biofilms on medical devices, Staphylococcus epidermidis has emerged as a major pathogen of nosocomial infections. In this study, we investigated the role of the two-component signal transduction system ArlRS in regulating S. epidermidis biofilm formation. An ArlRS-deficient mutant, WW06, was constructed using S. epidermidis strain 1457 as a parental strain. Although the growth curve of WW06 was similar to that of SE1457, the mutant strain was unable to form biofilms in vitro. In a rabbit subcutaneous infection model, sterile disks made of polymeric materials were implanted subcutaneously followed with inoculation of WW06 or SE1457. The viable bacteria cells of WW06 recovered from biofilms on the embedded disks were much lower than that of SE1457. Complementation of arlRS genes expression from plasmid in WW06 restored biofilm-forming phenotype both in vivo and in vitro. WW06 maintained the ability to undergo initial attachment. Transcription levels of several genes involved in biofilm formation, including icaADBC, sigB, and sarA, were decreased in WW06, compared to SE1457; and icaR expression was increased in WW06, detected by real-time reverse-transcription PCR. The biofilm-forming phenotype was restored by overexpressing icaADBC in WW06 but not by overexpressing sigB, indicating that ArlRS regulates biofilm formation through the regulation of icaADBC. Gel shift assay showed that ArlR can bind to the promoter region of the ica operon. In conclusion, ArlRS regulates S. epidermidis biofilm formation in an ica-dependent manner, distinct from its role in S. aureus.

  19. Performance Comparisions of ICA Algorithms to DS-CDMA Detection

    CERN Document Server

    Parmar, Sargam

    2010-01-01

    Commercial cellular networks, like the systems based on DS-CDMA, face many types of interferences such as multi-user interference inside each sector in a cell to interoperate interference. Independent Component Analysis (ICA) has been used as an advanced preprocessing tool for blind suppression of interfering signals in DS-CDMA communication systems. The role of ICA is to provide an interference-mitigated signal to the conventional detection. This paper evaluates the performance of some major ICA algorithms like Cardoso's joint approximate diagonalization of eigen matrices (JADE), Hyvarinen's fixed point algorithm and Comon's algorithm to solve the symbol estimation problem of the multi users in a DSCDMA communication system. The main focus is on blind separation of convolved CDMA mixture and the improvement of the downlink symbol estimation. The results of numerical experiment are compared with those obtained by the Single User Detection (SUD) receiver, ICA detector and combined SUD-ICA detector.

  20. A Reconfigurable FPGA System for Parallel Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Du Hongtao

    2006-01-01

    Full Text Available A run-time reconfigurable field programmable gate array (FPGA system is presented for the implementation of the parallel independent component analysis (ICA algorithm. In this work, we investigate design challenges caused by the capacity constraints of single FPGA. Using the reconfigurability of FPGA, we show how to manipulate the FPGA-based system and execute processes for the parallel ICA (pICA algorithm. During the implementation procedure, pICA is first partitioned into three temporally independent function blocks, each of which is synthesized by using several ICA-related reconfigurable components (RCs that are developed for reuse and retargeting purposes. All blocks are then integrated into a design and development environment for performing tasks such as FPGA optimization, placement, and routing. With partitioning and reconfiguration, the proposed reconfigurable FPGA system overcomes the capacity constraints for the pICA implementation on embedded systems. We demonstrate the effectiveness of this implementation on real images with large throughput for dimensionality reduction in hyperspectral image (HSI analysis.

  1. 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...... (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.......In chemicals based product manufacturing, as in pharmaceutical, food and agrochemical industries, efficient and consistent process monitoring and analysis systems (PAT systems) have a very important role. These PAT systems ensure that the chemicals based product is manufactured with the specified...

  2. Study of engine noise based on independent component analysis

    Institute of Scientific and Technical Information of China (English)

    HAO Zhi-yong; JIN Yan; YANG Chen

    2007-01-01

    Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic principle of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several independent components (Ics); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.

  3. Spectral Synthesis via Mean Field approach Independent Component Analysis

    CERN Document Server

    Hu, Ning; Kong, Xu

    2015-01-01

    In this paper, we apply a new statistical analysis technique, Mean Field approach to Bayesian Independent Component Analysis (MF-ICA), on galaxy spectral analysis. This algorithm can compress the stellar spectral library into a few Independent Components (ICs), and galaxy spectrum can be reconstructed by these ICs. Comparing to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, MF-ICA approach offers a large improvement in the efficiency. To check the reliability of this spectral analysis method, three different methods are used: (1) parameter-recover for simulated galaxies, (2) comparison with parameters estimated by other methods, and (3) consistency test of parameters from the Sloan Digital Sky Survey galaxies. We find that our MF-ICA method not only can fit the observed galaxy spectra efficiently, but also can recover the physical parameters of galaxies accurately. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find...

  4. ICA Model Order Estimation Using Clustering Method

    Directory of Open Access Journals (Sweden)

    P. Sovka

    2007-12-01

    Full Text Available In this paper a novel approach for independent component analysis (ICA model order estimation of movement electroencephalogram (EEG signals is described. The application is targeted to the brain-computer interface (BCI EEG preprocessing. The previous work has shown that it is possible to decompose EEG into movement-related and non-movement-related independent components (ICs. The selection of only movement related ICs might lead to BCI EEG classification score increasing. The real number of the independent sources in the brain is an important parameter of the preprocessing step. Previously, we used principal component analysis (PCA for estimation of the number of the independent sources. However, PCA estimates only the number of uncorrelated and not independent components ignoring the higher-order signal statistics. In this work, we use another approach - selection of highly correlated ICs from several ICA runs. The ICA model order estimation is done at significance level α = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters.

  5. Independent Component Analysis for Filtering Airwaves in Seabed Logging Application

    CERN Document Server

    Ansari, Adeel; Said, Abas B Md; Ansari, Seema

    2013-01-01

    Marine controlled source electromagnetic (CSEM) sensing method used for the detection of hydrocarbons based reservoirs in seabed logging application does not perform well due to the presence of the airwaves (or sea-surface). These airwaves interfere with the signal that comes from the subsurface seafloor and also tend to dominate in the receiver response at larger offsets. The task is to identify these air waves and the way they interact, and to filter them out. In this paper, a popular method for counteracting with the above stated problem scenario is Independent Component Analysis (ICA). Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional or multivariate dataset into its constituent components (sources) that are statistically as independent from each other as possible. ICA-type de-convolution algorithm that is FASTICA is considered for mixed signals de-convolution and considered convenient depending upon the nature of the source and noise model. The res...

  6. The Application of FastICA Combined with Related Function in Blind Signal Separation

    Directory of Open Access Journals (Sweden)

    Dengao Li

    2014-01-01

    Full Text Available Blind source separation (BSS has applications in the fields of data compression, feature recognition, speech, audio, and biosignal processing. Identification of ECG signal is one of the challenges in the biosignal processing. Proposed in this paper is a new method, which is the combination of related function relevance to estimated signal and negative entropy in fast independent component analysis (FastICA as objective function, and the iterative formula is derived without any assumptions; then the independent components are found by maximizing the objective function. The improved algorithm shorthand for R-FastICA is applied to extract random mixed signals and ventricular late potential (VLP signal from normal ECG signal; simultaneously the performance of R-FastICA algorithm is compared with traditional FastICA through simulation. Experimental results show that R-FastICA algorithm outperforms traditional FastICA with higher similarity coefficient and separation precision.

  7. Independent Component Analysis Over Galois Fields

    CERN Document Server

    Yeredor, Arie

    2010-01-01

    We consider the framework of Independent Component Analysis (ICA) for the case where the independent sources and their linear mixtures all reside in a Galois field of prime order P. Similarities and differences from the classical ICA framework (over the Real field) are explored. We show that a necessary and sufficient identifiability condition is that none of the sources should have a Uniform distribution. We also show that pairwise independence of the mixtures implies their full mutual independence (namely a non-mixing condition) in the binary (P=2) and ternary (P=3) cases, but not necessarily in higher order (P>3) cases. We propose two different iterative separation (or identification) algorithms: One is based on sequential identification of the smallest-entropy linear combinations of the mixtures, and is shown to be equivariant with respect to the mixing matrix; The other is based on sequential minimization of the pairwise mutual information measures. We provide some basic performance analysis for the bina...

  8. Watermark Detection and Extraction Using Independent Component Analysis Method

    Directory of Open Access Journals (Sweden)

    Yu Dan

    2002-01-01

    Full Text Available This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA for watermark detection and extraction process (i.e., dewatermarking. Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion.

  9. 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

    . Here we demonstrate that principal component analysis (PCA) followed by spatial independent component analysis (sICA), can be exploited to reduce the dimensionality of data sets recorded in the olfactory bulb and the somatosensory cortex of mice as well as the visual cortex of monkeys, without loosing...... 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...

  10. Implementation of pipelined FastICA on FPGA for real-time blind source separation.

    Science.gov (United States)

    Shyu, Kuo-Kai; Lee, Ming-Huan; Wu, Yu-Te; Lee, Po-Lei

    2008-06-01

    Fast independent component analysis (FastICA) algorithm separates the independent sources from their mixtures by measuring non-Gaussian. FastICA is a common offline method to identify artifact and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement FastICA for real-time signal processing. In this paper, the FastICA algorithm is implemented in a field-programmable gate array (FPGA), with the ability of real-time sequential mixed signals processing by the proposed pipelined FastICA architecture. Moreover, in order to increase the numbers precision, the hardware floating-point (FP) arithmetic units had been carried out in the hardware FastICA. In addition, the proposed pipeline FastICA provides the high sampling rate (192 kHz) capability by hand coding the hardware FastICA in hardware description language (HDL). To verify the features of the proposed hardware FastICA, simulations are first performed, then real-time signal processing experimental results are presented using the fabricated platform. Experimental results demonstrate the effectiveness of the presented hardware FastICA as expected.

  11. Blind breast tissue diagnosis using independent component analysis of localized backscattering response

    Science.gov (United States)

    Eguizabal, Alma; Laughney, Ashley M.; García Allende, Pilar Beatriz; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.; Lopez-Higuera, Jose M.; Conde, Olga M.

    2012-03-01

    A blind separation technique based on Independent Component Analysis (ICA) is proposed for breast tumor delineation and pathologic diagnosis. Tissue morphology is determined by fitting local measures of tissue reflectance to a Mie theory approximation, parameterizing the scattering power, scattering amplitude and average scattering irradiance. ICA is applied on the scattering parameters by spatial analysis using the Fast ICA method to extract more determinant features for an accurate diagnostic. Neither training, nor comparisons with reference parameters are required. Tissue diagnosis is provided directly following ICA application to the scattering parameter images. Surgically resected breast tissues were imaged and identified by a pathologist. Three different tissue pathologies were identified in 29 samples and classified as not-malignant, malignant and adipose. Scatter plot analysis of both ICA results and optical parameters where obtained. ICA subtle ameliorates those cases where optical parameter's scatter plots were not linearly separable. Furthermore, observing the mixing matrix of the ICA, it can be decided when the optical parameters themselves are diagnostically powerful. Moreover, contrast maps provided by ICA correlate with the pathologic diagnosis. The time response of the diagnostic strategy is therefore enhanced comparing with complex classifiers, enabling near real-time assessment of pathology during breast-conserving surgery.

  12. Efficient iris recognition via ICA feature and SVM classifier

    Institute of Scientific and Technical Information of China (English)

    Wang Yong; Xu Luping

    2007-01-01

    To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate, an iris recognition approach for combining SVM with ICA feature extraction model is presented. SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem. And ICA is a feature extraction technique which can be considered a generalization of principal component analysis. In this paper, ICA is used to generate a set of subsequences of feature vectors for iris feature extraction. Then each subsequence is classified using support vector machine sequence kernels. Experiments are made on CASIA iris database, the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.

  13. Independent component analysis in non-hypothesis driven metabolomics

    DEFF Research Database (Denmark)

    Li, Xiang; Hansen, Jakob; Zhao, Xinjie

    2012-01-01

    In a non-hypothesis driven metabolomics approach plasma samples collected at six different time points (before, during and after an exercise bout) were analyzed by gas chromatography-time of flight mass spectrometry (GC-TOF MS). Since independent component analysis (ICA) does not need a priori...

  14. Effects of erythromycin on the phenotypic and genotypic biofilm expression in two clinical Staphylococcus capitis subspecies and a functional analysis of Ica proteins in S. capitis.

    Science.gov (United States)

    Cui, Bintao; Smooker, Peter M; Rouch, Duncan A; Deighton, Margaret A

    2015-06-01

    The ica operon encoding polysaccharide intercellular adhesion, which facilitates biofilm formation in staphylococci, has been extensively studied in Staphylococcus epidermidis and Staphylococcus aureus. Based on in silico analysis, we suggest the following functional model for Ica proteins in S. capitis. IcaA is responsible for polysaccharide synthesis. IcaA and IcaD complete transferring the growing sugar chain to the cell surface; IcaB is a deacetylase, with the same function as IcaB of S. epidermidis. IcaC mainly modifies the synthesized glucan by acetylation. We also examined the effects of subinhibitory concentrations of erythromycin on phenotypic biofilm expression and transcription of biofilm-related genes, using isolates representing the two subspecies of Staphylococcus capitis and different biofilm and resistance phenotypes. On induction with erythromycin, biofilm density was strongly elevated in two erythromycin-resistant S. capitis, but not in three susceptible isolates. In the representative erythromycin-resistant S. capitis subsp. urealyticus, there were significant upregulations of the icaA gene and its positive regulator sarA on transition to the stationary phase without erythromycin induction. There were also significant increases in the transcription levels of icaA, rsbU and sigB corresponding to a very strong biofilm phenotype in the stationary phase on erythromycin stress. In contrast, the representative erythromycin-susceptible S. capitis subsp. capitis displayed upregulation only of altE on entry into the stationary phase with erythromycin induction, but this change was not associated with enhancement of biofilm production. These findings suggest that the two subspecies of S. capitis adopt different pathogenesis and survival strategies to adapt to a hostile environment.

  15. 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

  16. [Independent component analysis for spectral unmixing in hyperspectral remote sensing image].

    Science.gov (United States)

    Luo, Wen-Fei; Zhong, Liang; Zhang, Bing; Gao, Lian-Ru

    2010-06-01

    Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral un-mixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to un-mix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM. This embarrassment compromises ICA applicability to hyperspectral data. To overcome this problem, the present paper introduces a solution of minimization of total correlation of the components. Interestingly, with the minimization of total correlation of the components, the angle of the direction between each components is invariable. A Parallel oblique-ICA (Pob-ICA) algorithm is proposed to correct the angle of the searching direction between the components. Two novelties result from our proposed Pob-ICA algorithm. First, the algorithm completely satisfies the physical constraint conditions in LSMM and overcomes the limitation of statistical independency assumed by ICA. Second, the last component, which is missed in other existing ICA algorithms, can be estimated by our proposed algorithm. In experiments, Pob-ICA algorithm demonstrates excellent performance in the simulative and real hyperspectral images.

  17. Independent component analysis of Alzheimer's DNA microarray gene expression data

    Directory of Open Access Journals (Sweden)

    Vanderburg Charles R

    2009-01-01

    Full Text Available Abstract Background Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA and independent component analysis (ICA have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics. Results ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In

  18. Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix.

    Science.gov (United States)

    Naik, Ganesh R; Acharyya, Amit; Nguyen, Hung T

    2014-01-01

    This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.

  19. 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

    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 Analysi...... 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.......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...

  20. Joint ICA-Based Blind Detection and Parameter Assessment in DS-CDMA Systems

    Directory of Open Access Journals (Sweden)

    Mohammad Eslami

    2011-09-01

    Full Text Available In this study, blind code extraction of Direct Sequence Code Division Multiple Access (DS-CDMA signals is considered, based on Independent Component Analysis (ICA method. In order to distinguish between correct and incorrect extracted codes, to estimate the number of active users and also to determine the quality of detection along with the ICA based blind detection procedure, some propositions are defined. These propositions are used to improve the performance of the ICA blind detection based method. Then, in order to analyze the proposed criteria, Principle Component Analysis (PCA and Gaussian Mixture Model (GMM are employed. Experimental results illustrate that the achieved performance of the defined criteria.

  1. Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process

    Institute of Scientific and Technical Information of China (English)

    CHEN Hua-fu; YAO De-zhong

    2005-01-01

    One important application of independent component analysis (ICA) is in image processing. A two dimensional (2-D) composite ICA algorithm framework for 2-D image independent component analysis (2-D ICA) is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional (2-D) image data and one-dimensional (1-D) signal. Moreover the combination of the Newton (fixed-point algorithm) and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging (fMRI) show the potential application of composite 2-D ICA in the brain activity detection.

  2. A fMRI Data Processing Method Using a New Composite ICA Algorithm

    Institute of Scientific and Technical Information of China (English)

    CHENHuafu; ZHOUQun; YAODezhong; ZENGMin

    2004-01-01

    One of the reasons Independent component analysis (ICA) becoming so popular is that ICA is a promising tool for signal process application, such as Functional magnetic resonance imaging (fMRI) data processing. However, there are still some problems to be solved. Most ICA algorithms are not stable in fMRI data processing. This paper presents a novel composite ICA algorithm integrating fixed-point algorithm and natural gradient algorithm for brain activity localization in Functional magnetic resonance imaging (fMRI) data. The new composite ICA algorithm has overcome the drawbacks of the both algorithms, providing more accurate and fast detection of weak fMRI functional signals. Simulations show great performance improvement compared with correlation analysis and Automated functional neuro imaging (AfNI) software.

  3. An Automated Video Object Extraction System Based on Spatiotemporal Independent Component Analysis and Multiscale Segmentation

    Directory of Open Access Journals (Sweden)

    Zhang Xiao-Ping

    2006-01-01

    Full Text Available Video content analysis is essential for efficient and intelligent utilizations of vast multimedia databases over the Internet. In video sequences, object-based extraction techniques are important for content-based video processing in many applications. In this paper, a novel technique is developed to extract objects from video sequences based on spatiotemporal independent component analysis (stICA and multiscale analysis. The stICA is used to extract the preliminary source images containing moving objects in video sequences. The source image data obtained after stICA analysis are further processed using wavelet-based multiscale image segmentation and region detection techniques to improve the accuracy of the extracted object. An automated video object extraction system is developed based on these new techniques. Preliminary results demonstrate great potential for the new stICA and multiscale-segmentation-based object extraction system in content-based video processing applications.

  4. 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.

  5. Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA

    Institute of Scientific and Technical Information of China (English)

    You Rong-Yi; Chen Zhong

    2005-01-01

    Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.

  6. Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA

    Science.gov (United States)

    You, Rong-Yi; Chen, Zhong

    2005-11-01

    Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.

  7. 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.

  8. Role of diversity in ICA and IVA: theory and applications

    Science.gov (United States)

    Adalı, Tülay

    2016-05-01

    Independent component analysis (ICA) has been the most popular approach for solving the blind source separation problem. Starting from a simple linear mixing model and the assumption of statistical independence, ICA can recover a set of linearly-mixed sources to within a scaling and permutation ambiguity. It has been successfully applied to numerous data analysis problems in areas as diverse as biomedicine, communications, finance, geo- physics, and remote sensing. ICA can be achieved using different types of diversity—statistical property—and, can be posed to simultaneously account for multiple types of diversity such as higher-order-statistics, sample dependence, non-circularity, and nonstationarity. A recent generalization of ICA, independent vector analysis (IVA), generalizes ICA to multiple data sets and adds the use of one more type of diversity, statistical dependence across the data sets, for jointly achieving independent decomposition of multiple data sets. With the addition of each new diversity type, identification of a broader class of signals become possible, and in the case of IVA, this includes sources that are independent and identically distributed Gaussians. We review the fundamentals and properties of ICA and IVA when multiple types of diversity are taken into account, and then ask the question whether diversity plays an important role in practical applications as well. Examples from various domains are presented to demonstrate that in many scenarios it might be worthwhile to jointly account for multiple statistical properties. This paper is submitted in conjunction with the talk delivered for the "Unsupervised Learning and ICA Pioneer Award" at the 2016 SPIE Conference on Sensing and Analysis Technologies for Biomedical and Cognitive Applications.

  9. Principal component analysis

    NARCIS (Netherlands)

    Bro, R.; Smilde, A.K.

    2014-01-01

    Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis

  10. A post-modification approach to independent component analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

    Institute of Scientific and Technical Information of China (English)

    WANG Wei; CAI WenSheng; SHAO XueGuang

    2007-01-01

    Independent component analysis (ICA) has demonstrated its power to extract mass spectra from overlapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is proposed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.

  11. Edge detection algorithm based on ICA-domain shrinkage in noisy images

    Institute of Scientific and Technical Information of China (English)

    HAN XianHua; DAI ShuiYan; LI Jian; XIA GuoRong

    2008-01-01

    We propose a robust edge detection method based on ICA-domain shrinkage (independent component analysis). It is known that most basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in ICA-domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.

  12. 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.

  13. Optimizing preprocessing and analysis pipelines for single-subject fMRI: 2. Interactions with ICA, PCA, task contrast and inter-subject heterogeneity.

    Science.gov (United States)

    Churchill, Nathan W; Yourganov, Grigori; Oder, Anita; Tam, Fred; Graham, Simon J; Strother, Stephen C

    2012-01-01

    A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or "pipeline") may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a

  14. 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.

  15. 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.

  16. Study on application of independent component analysis in the CSNS/RCS

    Science.gov (United States)

    An, Yu-Wen; Wang, Sheng

    2013-03-01

    The China Spallation Neutron Source (CSNS) accelerators consist of a low energy H- linac and a high energy proton Rapid Cycling Synchrotron (RCS). The proton beam is accumulated in the RCS and accelerated from 80 MeV to 1.6 GeV with a repetition of 25 Hz. Independent component analysis (ICA) is a robust method for processing the collected data (samples) recorded by the turn-by-turn beam position monitor (BPM), which was recently applied to the accelerator. The samples are decomposed to source signals, or the so-called independent components, which correspond to the inherent motion of samples, such as betatron motion and synchrotron motion. A study on the application of the ICA method to CSNS/RCS has been made. It shows that the beta function, phase advance, and dispersion can be well reconstructed by using ICA in CSNS/RCS. The effects of BPM errors on the ICA results are also studied. By comparing the different solving methods in ICA, the so-called SOBI has more advantages for isolating the independent components on the application of ICA to CSNS/RCS. Beam emittance dilution in the process of exciting the turn-by-turn samples is considered, and thus an RF kicker is adopted to avoid such emittance growth.

  17. Fixed-point blind source separation algorithm based on ICA

    Institute of Scientific and Technical Information of China (English)

    Hongyan LI; Jianfen MA; Deng'ao LI; Huakui WANG

    2008-01-01

    This paper introduces the fixed-point learning algorithm based on independent component analysis (ICA);the model and process of this algorithm and simulation results are presented.Kurtosis was adopted as the estimation rule of independence.The results of the experiment show that compared with the traditional ICA algorithm based on random grads,this algorithm has advantages such as fast convergence and no necessity for any dynamic parameter,etc.The algorithm is a highly efficient and reliable method in blind signal separation.

  18. Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis.

    Science.gov (United States)

    Kanoga, Suguru; Mitsukura, Yasue

    2015-01-01

    To study an eye blink artifact rejection scheme from single-channel electroencephalographic (EEG) signals has been now a major challenge in the field of EEG signal processing. High removal performance is still needed to more strictly investigate pattern of EEG features. This paper proposes a new eye blink artifact rejection scheme from single-channel EEG signals by combining complete ensemble empirical mode decomposition (CEEMD) and independent component analysis (ICA). We compare the separation performance of our proposed scheme with existing schemes (wavelet-ICA, EMD-ICA, and EEMD-ICA) though real-life data by using signal-to-noise ratio. As a result, CEEMD-ICA showed high performance (11.86 dB) than all other schemes (10.78, 10.59, and 11.30 dB) in the ability of eye blink artifact removal.

  19. Feature Extraction Using Supervised Independent Component Analysis by Maximizing Class Distance

    Science.gov (United States)

    Sakaguchi, Yoshinori; Ozawa, Seiichi; Kotani, Manabu

    Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of pattern features extracted by conventional ICA algorithms depends on pattern sets; that is, how patterns are distributed in the feature space. As one of the reasons, we have pointed out that ICA features are obtained by increasing only their independence even if the class information is available. In this context, we can expect that more high-performance features can be obtained by introducing the class information into conventional ICA algorithms. In this paper, we propose a supervised ICA (SICA) that maximizes Mahalanobis distance between features of different classes as well as maximize their independence. In the first experiment, two-dimensional artificial data are applied to the proposed SICA algorithm to see how maximizing Mahalanobis distance works well in the feature extraction. As a result, we demonstrate that the proposed SICA algorithm gives good features with high separability as compared with principal component analysis and a conventional ICA. In the second experiment, the recognition performance of features extracted by the proposed SICA is evaluated using the three data sets of UCI Machine Learning Repository. From the results, we show that the better recognition accuracy is obtained using our proposed SICA. Furthermore, we show that pattern features extracted by SICA are better than those extracted by only maximizing the Mahalanobis distance.

  20. 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.

  1. Space distribution of EEG responses to hanoi-moving visual and auditory stimulation with Fourier Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Shijun eLi

    2015-07-01

    Full Text Available Background and objective: The relationship between EEG source signals and action-related visual and auditory stimulation is still not well understood. The objective of this study was to identify EEG source signals and their associated action-related visual and auditory responses, especially independent components of EEG.Methods: A hand-moving-Hanoi video paradigm was used to study neural correlates of the action-related visual and auditory information processing determined by mu rhythm (8-12 Hz in 16 healthy young subjects. Independent component analysis (ICA was applied to identify separate EEG sources, and further computed in the frequency domain by applying-Fourier transform ICA (F-ICA.Results: F-ICA found more sensory stimuli-related independent components located within the sensorimotor region than ICA did. The total number of independent components of interest from F-ICA was 768, twice that of 384 from traditional time-domain ICA (p0.05.Conclusions: These results support the hypothesis that mu rhythm was sensitive to detection of the cognitive expression, which could be reflected by the function in the parietal lobe sensory-motor region. The results of this study could potentially be applied into early diagnosis for those with visual and hearing impairments in the future.

  2. Active Shape Model of Combining Pca and Ica: Application to Facial Feature Extraction

    Institute of Scientific and Technical Information of China (English)

    DENG Lin; RAO Ni-ni; WANG Gang

    2006-01-01

    Active Shape Model (ASM) is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis (PCA) to statistically model the variability in the training set of example shapes. Independent Component Analysis (ICA) has been proven to be more efficient to extract face features than PCA . In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.

  3. 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....... The mixing is represented by “mixture coefficient images” quantifying the local response to a given source at a certain time lag. This is the first communication to address this important issue in the context of fMRI ICA. Data: A single slice holding 128x128 pixels and passing through primary visual cortex......Modeling & Analysis Abstract The fMRI signal has many sources: Stimulus induced activation, other brain activations, confounds including several physiological signal components, the most prominent being the cardiac pulsation at about 1 Hz, and breathing induced motion (0.2-1 Hz). Most fMRI data...

  4. Finding Pathogenicity Islands in Genome Data with ICA

    Institute of Scientific and Technical Information of China (English)

    ZHENG Fangwei; HUANG Juncai; SHE Kun; ZHOU Mingtian

    2004-01-01

    A novel technique for finding pathogenicity islands in genome data with independent component analyses(ICA) is present.First denoise the genomic signal sequences with ICA and detect G+C patterns in genomes by comparing the result sequence with original sequences.The results on G+C patterns analysis of Dradiodurans chromosome I and N.serogroup A strain Z2491 are present.A set of loci that have very different G+C content and have not previously described are detected.The findings show that ICA is a powerful tool to detect differences within and between genomes and to separate small (gene level) and large (putative pathogenicity islands) genomic regions that have different composition characteristics.

  5. The Application of ICA to Quantitative Analysis by Near Infrared Reflectance Spectroscopy%独立分量分析在近红外光谱定量分析中的应用

    Institute of Scientific and Technical Information of China (English)

    谢秀娟; 赵龙莲

    2012-01-01

    Independent component analysis was applied to decompose the independent components in near infrared spectral of corn powder samples. Each component spectrum obtained was statistically independent. And quantitative analysis model based on ICA constituents of corn protein, starch and crude fat content was established by means of multiple regressions. The correlation coefficients between the prediction value by NIRS and chemical analysis results of the three components in modeling set and prediction set were relatively high and the mean relative errors were lower. The result shows that the accuracy of the NIR model based on ICA to analysis three main components of the corn samples is high comparatively, and the model can be used in quality analysis in large quantities of maize breeding samples.%采用独立分量分析(ICA)方法,对玉米样品的近红外光谱进行分解,得到统计上独立的各成分光谱;然后用多元回归方法建立基于ICA成分的玉米粗蛋白质、粗淀粉和粗脂肪含量的定量分析模型,3种成分建模集和预测集的化学值和近红外预测值之间的相关系数都较高,且平均相对误差都较低.结果表明,ICA方法建立的玉米样品3个主要成分的近红外模型预测准确度都较高,可应用于玉米育种中大批样品的快速品质分析.

  6. Extracting invariable fault features of rotating machines with multi-ICA networks

    Institute of Scientific and Technical Information of China (English)

    焦卫东; 杨世锡; 吴昭同

    2003-01-01

    This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together.Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.

  7. Spectral Synthesis via Mean Field approach to Independent Component Analysis

    Science.gov (United States)

    Hu, Ning; Su, Shan-Shan; Kong, Xu

    2016-03-01

    We apply a new statistical analysis technique, the Mean Field approach to Independent Component Analysis (MF-ICA) in a Bayseian framework, to galaxy spectral analysis. This algorithm can compress a stellar spectral library into a few Independent Components (ICs), and the galaxy spectrum can be reconstructed by these ICs. Compared to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, the MF-ICA approach offers a large improvement in efficiency. To check the reliability of this spectral analysis method, three different methods are used: (1) parameter recovery for simulated galaxies, (2) comparison with parameters estimated by other methods, and (3) consistency test of parameters derived with galaxies from the Sloan Digital Sky Survey. We find that our MF-ICA method can not only fit the observed galaxy spectra efficiently, but can also accurately recover the physical parameters of galaxies. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find it can provide excellent fitting results for low signal-to-noise spectra.

  8. State Inspection for Transmission Lines Based on Independent Component Analysis

    Institute of Scientific and Technical Information of China (English)

    REN Li-jia; JIANG Xiu-chen; SHENG Ge-hao; YANG Wei-wei

    2009-01-01

    Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation. Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods, and it is applicable to extract the non-stationary signals. FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper. A new method combined empirical mode decomposition (EMD) technique with the adaptive threshold method is applied to extract the vibration pulses, and suppress the interference signals. The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.

  9. Independent component analysis based source number estimation and its comparison for mechanical systems

    Science.gov (United States)

    Cheng, Wei; Lee, Seungchul; Zhang, Zhousuo; He, Zhengjia

    2012-11-01

    It has been challenging to correctly separate the mixed signals into source components when the source number is not known a priori. In this paper, we propose a novel source number estimation based on independent component analysis (ICA) and clustering evaluation analysis. We investigate and benchmark three information based source number estimations: Akaike information criterion (AIC), minimum description length (MDL) and improved Bayesian information criterion (IBIC). All the above methods are comparatively studied in both numerical and experimental case studies with typical mechanical signals. The results demonstrate that the proposed ICA based source number estimation with nonlinear dissimilarity measures performs more stable and robust than the information based ones for mechanical systems.

  10. Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming.

    Science.gov (United States)

    Craddock, Matt; Martinovic, Jasna; Müller, Matthias M

    2016-04-01

    Neuronal activity in the gamma-band range was long considered a marker of object representation. However, scalp-recorded EEG activity in this range is contaminated by a miniature saccade-related muscle artifact. Independent component analysis (ICA) has been proposed as a method of removal of such artifacts. Alternatively, beamforming, a source analysis method in which potential sources of activity across the whole brain are scanned independently through the use of adaptive spatial filters, offers a promising method of accounting for the artifact without relying on its explicit removal. We present here the application of ICA-based correction to a previously published dataset. Then, using beamforming, we examine the effect of ICA correction on the scalp-recorded EEG signal and the extent to which genuine activity is recoverable before and after ICA correction. We find that beamforming attributes much of the scalp-recorded gamma-band signal before correction to deep frontal sources, likely the eye muscles, which generate the artifact related to each miniature saccade. Beamforming confirms that what is removed by ICA is predominantly this artifactual signal, and that what remains after correction plausibly originates in the visual cortex. Thus, beamforming allows researchers to confirm whether their removal procedures successfully removed the artifact. Our results demonstrate that ICA-based correction brings about general improvements in signal-to-noise ratio suggesting it should be used along with, rather than be replaced by, beamforming.

  11. Text Classification Retrieval Based on Complex Network and ICA Algorithm

    Directory of Open Access Journals (Sweden)

    Hongxia Li

    2013-08-01

    Full Text Available With the development of computer science and information technology, the library is developing toward information and network. The library digital process converts the book into digital information. The high-quality preservation and management are achieved by computer technology as well as text classification techniques. It realizes knowledge appreciation. This paper introduces complex network theory in the text classification process and put forwards the ICA semantic clustering algorithm. It realizes the independent component analysis of complex network text classification. Through the ICA clustering algorithm of independent component, it realizes character words clustering extraction of text classification. The visualization of text retrieval is improved. Finally, we make a comparative analysis of collocation algorithm and ICA clustering algorithm through text classification and keyword search experiment. The paper gives the clustering degree of algorithm and accuracy figure. Through simulation analysis, we find that ICA clustering algorithm increases by 1.2% comparing with text classification clustering degree. Accuracy can be improved by 11.1% at most. It improves the efficiency and accuracy of text classification retrieval. It also provides a theoretical reference for text retrieval classification of eBook

  12. Elucidating the altered transcriptional programs in breast cancer using independent component analysis.

    Directory of Open Access Journals (Sweden)

    Andrew E Teschendorff

    2007-08-01

    Full Text Available The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis (ICA is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis (PCA, a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation. We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial-mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors (NF1 and NFAT with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype-pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases.

  13. Structure learning by pruning in independent component analysis

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Hansen, Lars Kai

    2008-01-01

    We discuss pruning as a means of structure learning in independent component analysis (ICA). Learning the structure is attractive in both signal processing and in analysis of abstract data, where it can assist model interpretation, generalizability and reduce computation. We derive the relevant s...... based methods and Bayesian methods, for both small and large samples. The Bayesian information criterion (BIC) seems to outperform both AIC and test sets as tools for determining the optimal dimensionality....... saliency expressions and compare with magnitude based pruning and Bayesian sparsification. We show in simulations that pruning is able to identify underlying structures without prior knowledge on the dimensionality of the model. We find, that for ICA, magnitude based pruning is as efficient as saliency...

  14. Multilevel component analysis

    NARCIS (Netherlands)

    Timmerman, M.E.

    2006-01-01

    A general framework for the exploratory component analysis of multilevel data (MLCA) is proposed. In this framework, a separate component model is specified for each group of objects at a certain level. The similarities between the groups of objects at a given level can be expressed by imposing cons

  15. Independent component analysis classification of laser induced breakdown spectroscopy spectra

    Energy Technology Data Exchange (ETDEWEB)

    Forni, Olivier, E-mail: olivier.forni@irap.omp.eu [Université de Toulouse, UPS-OMP, Institut de Recherche en Astrophysiqe et Planétologie, Toulouse (France); CNRS, IRAP, 9, av. Colonel Roche, BP 44346, F-31028 Cedex 4, Toulouse (France); Maurice, Sylvestre, E-mail: sylvestre.maurice@irap.omp.eu [Université de Toulouse, UPS-OMP, Institut de Recherche en Astrophysiqe et Planétologie, Toulouse (France); CNRS, IRAP, 9, av. Colonel Roche, BP 44346, F-31028 Cedex 4, Toulouse (France); Gasnault, Olivier, E-mail: olivier.gasnault@irap.omp.eu [Université de Toulouse, UPS-OMP, Institut de Recherche en Astrophysiqe et Planétologie, Toulouse (France); CNRS, IRAP, 9, av. Colonel Roche, BP 44346, F-31028 Cedex 4, Toulouse (France); Wiens, Roger C., E-mail: rwiens@lanl.gov [Space Remote Sensing, Los Alamos National Laboratory, Los Alamos, NM 87544 (United States); Cousin, Agnès, E-mail: acousin@lanl.gov [Université de Toulouse, UPS-OMP, Institut de Recherche en Astrophysiqe et Planétologie, Toulouse (France); CNRS, IRAP, 9, av. Colonel Roche, BP 44346, F-31028 Cedex 4, Toulouse (France); Chemical Division, Los Alamos National Laboratory, Los Alamos, NM 87544 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Chemical Division, Los Alamos National Laboratory, Los Alamos, NM 87544 (United States); Sirven, Jean-Baptiste, E-mail: jean-baptiste.sirven@cea.f [CEA Saclay, DEN/DPC/SCP, 91191 Cedex, Gif sur Yvette (France); Lasue, Jérémie, E-mail: jeremie.lasue@irap.omp.eu [Université de Toulouse, UPS-OMP, Institut de Recherche en Astrophysiqe et Planétologie, Toulouse (France); CNRS, IRAP, 9, av. Colonel Roche, BP 44346, F-31028 Cedex 4, Toulouse (France)

    2013-08-01

    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.

  16. On the independent component analysis of evoked potentials through single or few recording channels.

    Science.gov (United States)

    Wang, Suogang; James, Christopher J

    2007-01-01

    In this work we propose a technique based on Independent Component Analysis (ICA), applied to single or two channel(s) recordings of electroencephalogram (EEG) brain signals. Standard (ensemble) ICA requires multiple channel recordings to work, however when single of few channels are required ensemble ICA cannot be readily applied. Single channel ICA (temporal ICA) can be performed by preprocessed the data using the method of delays. Few channels (space-time ICA) can be analysed in an extension to this method. These techniques are demonstrated on the P300 evoked potentials (EPs) of a brain-computer interfacing (BCI) word speller dataset. We furthermore show how it is possible to extract single trial evoked EPs (i.e. non-stimulus locked) within a little as 3 epochs and even on channels not over the event focus. Due to the poor SNR, as well as the presence of other artifacts, it is difficult to detect the P300 pattern on raw signal data. The results show that proposed algorithms are able to accurately and repeatedly extract the relevant information buried within noisy signals and to do so without the requirement of stimulus locked averages. These advantages are paramount for building a more reliable and robust system for use in real-world BCI--i.e. for use outside of the clinical laboratory.

  17. A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies.

    Science.gov (United States)

    Guo, Ying; Tang, Li

    2013-12-01

    An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this article, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, for example, subjects with mental disorders or neurodegenerative diseases such as Parkinson's as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation.

  18. Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

    Institute of Scientific and Technical Information of China (English)

    Ajami Ali; Daneshvar Mahdi

    2014-01-01

    A statistical signal processing technique was proposed and verified as independent component analysis (ICA) for fault detection and diagnosis of industrial systems without exact and detailed model. Actually, the aim is to utilize system as a black box. The system studied is condenser system of one of MAPNA’s power plants. At first, principal component analysis (PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones. Then, the fault sources were diagnosed by ICA technique. The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states, and it can distinguish main factors of abnormality among many diverse parts of a power plant’s condenser system. This selectivity problem is left unsolved in many plants, because the main factors often become unnoticed by fault expansion through other parts of the plants.

  19. A new process monitoring method based on noisy time structure independent component analysis

    Institute of Scientific and Technical Information of China (English)

    Lianfang Cai; Xuemin Tian

    2015-01-01

    Conventional process monitoring method based on fast independent component analysis (FastICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA (NoisyTSICA) is proposed to solve such problem. A NoisyTSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components (ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recur-sive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed NoisyTSICA-based monitoring method outperforms the conven-tional FastICA-based monitoring method.

  20. Independent components in spectroscopic analysis of complex mixtures

    CERN Document Server

    Monakhova, Yulia B; Kraskov, Alexander; Mushtakova, Svetlana P; 10.1016/j.chemolab.2010.05.023

    2010-01-01

    We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to quantitative and qualitative analysis of UV absorption spectra of several non-trivial mixture types. Both methods use the concept of statistical independence and aim at the reconstruction of minimally dependent components from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a veterinary drug. Both MICLA and SNICA were able to recover concentrations and individual spectra with minimal errors comparable with instrumental noise. In most cases their performance was similar to or better than that of other chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA. These results suggest that the ICA methods used in this study are suitable for real life applications.

  1. Discriminant Incoherent Component Analysis.

    Science.gov (United States)

    Georgakis, Christos; Panagakis, Yannis; Pantic, Maja

    2016-05-01

    Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions, and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and AU activations. In this paper, the discriminant incoherent component analysis (DICA) is proposed in order to extract low-complexity components, corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, and AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and l1 -norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely, joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and AU detection by conducting experiments on four data sets. The proposed method outperforms all the methods that are compared with all the tasks and experimental settings.

  2. Modeling Molecular Kinetics with tICA and the Kernel Trick

    Science.gov (United States)

    2016-01-01

    The allure of a molecular dynamics simulation is that, given a sufficiently accurate force field, it can provide an atomic-level view of many interesting phenomena in biology. However, the result of a simulation is a large, high-dimensional time series that is difficult to interpret. Recent work has introduced the time-structure based Independent Components Analysis (tICA) method for analyzing MD, which attempts to find the slowest decorrelating linear functions of the molecular coordinates. This method has been used in conjunction with Markov State Models (MSMs) to provide estimates of the characteristic eigenprocesses contained in a simulation (e.g., protein folding, ligand binding). Here, we extend the tICA method using the kernel trick to arrive at nonlinear solutions. This is a substantial improvement as it allows for kernel-tICA (ktICA) to provide estimates of the characteristic eigenprocesses directly without building an MSM. PMID:26528090

  3. 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.

  4. 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.

  5. Robust Principal Component Analysis?

    CERN Document Server

    Candes, Emmanuel J; Ma, Yi; Wright, John

    2009-01-01

    This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for th...

  6. Cognitive Component Analysis

    DEFF Research Database (Denmark)

    Feng, Ling

    2008-01-01

    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......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...... 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...

  7. 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)

  8. Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Suogang Wang

    2007-01-01

    Full Text Available We propose a technique based on independent component analysis (ICA with constraints, applied to the rhythmic electroencephalographic (EEG data recorded from a brain-computer interfacing (BCI system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.

  9. GNSS Vertical Coordinate Time Series Analysis Using Single-Channel Independent Component Analysis Method

    Science.gov (United States)

    Peng, Wei; Dai, Wujiao; Santerre, Rock; Cai, Changsheng; Kuang, Cuilin

    2017-02-01

    Daily vertical coordinate time series of Global Navigation Satellite System (GNSS) stations usually contains tectonic and non-tectonic deformation signals, residual atmospheric delay signals, measurement noise, etc. In geophysical studies, it is very important to separate various geophysical signals from the GNSS time series to truthfully reflect the effect of mass loadings on crustal deformation. Based on the independence of mass loadings, we combine the Ensemble Empirical Mode Decomposition (EEMD) with the Phase Space Reconstruction-based Independent Component Analysis (PSR-ICA) method to analyze the vertical time series of GNSS reference stations. In the simulation experiment, the seasonal non-tectonic signal is simulated by the sum of the correction of atmospheric mass loading and soil moisture mass loading. The simulated seasonal non-tectonic signal can be separated into two independent signals using the PSR-ICA method, which strongly correlated with atmospheric mass loading and soil moisture mass loading, respectively. Likewise, in the analysis of the vertical time series of GNSS reference stations of Crustal Movement Observation Network of China (CMONOC), similar results have been obtained using the combined EEMD and PSR-ICA method. All these results indicate that the EEMD and PSR-ICA method can effectively separate the independent atmospheric and soil moisture mass loading signals and illustrate the significant cause of the seasonal variation of GNSS vertical time series in the mainland of China.

  10. EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

    Directory of Open Access Journals (Sweden)

    Tom Eichele

    2011-01-01

    Full Text Available Independent component analysis (ICA is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.

  11. Analysis of the biofilm formation and icaA genes of Staphylococcus epidermidis isolates%表皮葡萄球菌临床株生物被膜形成及其icaA基因的分析

    Institute of Scientific and Technical Information of China (English)

    于树云; 宋诗铎

    2009-01-01

    Objective To determine the ability of biofilm formation of Staphylococcus ephtermidis isolates and analyze the correlation between the icaA gene and its expression and biofilm formation. Methods Collecting 205 Staphylococcus epidermidis isolates identified with normal laboratory tests (coagulase-negative, biochemical identification, polymyxin-resistant and novobiocin-sensitive ), the suspected isolates were con-formed with API-Staph. Biofilm production was assessed by incubating the strains on Congo Red Agar (CRA) plates and quantitative biofilm production determined by a 96-well tissue culture plate and biofilm morphous were detected by scanning electron microscope ( SEM ) ; Amplifying partial fragments of icaA genes with PCR; Analyzing the expression levels of icaA gene with RT-PCR through Bio-Rad system and Quantity One software. Results 24 isolates showed positive in CRA tests, 22 isolates were positive in semiquantita- tive adhesion assays and 28 isolates existed icaA gene among 205 isolates of Staphylococcus epidermidis. The icaA-positive strains demonstrated biofilm formation (microcolonies on silica films ) while icaA-negative strains only adhered as individual cells under scanning electron microscope. All 22 strains which showed positive in semiquantitative adhesion assays harbored the icaA gene. The expression levels of icaA gene with RT-PCR in 6 Staphylococcus epidermidis isolates showed a higher tendency in 4 strains which demonstrated positive in semiquantitative adhesion assays than 2 negative strains in semiquantitative adhesion assays. Conclusion The isolations of Staphylococcus epidermidis have the abilities of forming biofilm, and the icaA gene and its normal expression is the important molecular biology foundation of biofilm formation. Other fac-tors maybe involve in the expression of icaA gene in Staphylococcus epidermidis isolates.%目的 检测表皮葡萄球菌临床株生物被膜的形成能力,了解icaA基因及其表达与生

  12. 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...... in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization....

  13. Similar component analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hong; WANG Xin; LI Junwei; CAO Xianguang

    2006-01-01

    A new unsupervised feature extraction method called similar component analysis (SCA) is proposed in this paper. SCA method has a self-aggregation property that the data objects will move towards each other to form clusters through SCA theoretically,which can reveal the inherent pattern of similarity hidden in the dataset. The inputs of SCA are just the pairwise similarities of the dataset,which makes it easier for time series analysis due to the variable length of the time series. Our experimental results on many problems have verified the effectiveness of SCA on some engineering application.

  14. Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages

    DEFF Research Database (Denmark)

    Lukic, Ana S.; Wernick, Miles N.; Yang, Yongui;

    2007-01-01

    It has been previously observed that spatial independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper we seek to determine analytically the conditions under which...... this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (f......MRI) data. Our analysis is applicable to both inter-subject and intra-subject spatial normalization....

  15. Applying independent component analysis to detect silent speech in magnetic resonance imaging signals.

    Science.gov (United States)

    Abe, Kazuhiro; Takahashi, Toshimitsu; Takikawa, Yoriko; Arai, Hajime; Kitazawa, Shigeru

    2011-10-01

    Independent component analysis (ICA) can be usefully applied to functional imaging studies to evaluate the spatial extent and temporal profile of task-related brain activity. It requires no a priori assumptions about the anatomical areas that are activated or the temporal profile of the activity. We applied spatial ICA to detect a voluntary but hidden response of silent speech. To validate the method against a standard model-based approach, we used the silent speech of a tongue twister as a 'Yes' response to single questions that were delivered at given times. In the first task, we attempted to estimate one number that was chosen by a participant from 10 possibilities. In the second task, we increased the possibilities to 1000. In both tasks, spatial ICA was as effective as the model-based method for determining the number in the subject's mind (80-90% correct per digit), but spatial ICA outperformed the model-based method in terms of time, especially in the 1000-possibility task. In the model-based method, calculation time increased by 30-fold, to 15 h, because of the necessity of testing 1000 possibilities. In contrast, the calculation time for spatial ICA remained as short as 30 min. In addition, spatial ICA detected an unexpected response that occurred by mistake. This advantage was validated in a third task, with 13 500 possibilities, in which participants had the freedom to choose when to make one of four responses. We conclude that spatial ICA is effective for detecting the onset of silent speech, especially when it occurs unexpectedly.

  16. Spatiotemporal filtering for regional GPS network in China using independent component analysis

    Science.gov (United States)

    Ming, Feng; Yang, Yuanxi; Zeng, Anmin; Zhao, Bin

    2017-04-01

    Removal of the common mode error (CME) is a routine procedure in postprocessing regional GPS network observations, which is commonly performed using principal component analysis (PCA). PCA decomposes a network time series into a group of modes, where each mode comprises a common temporal function and corresponding spatial response based on second-order statistics (variance and covariance). However, the probability distribution function of a GPS time series is non-Gaussian; therefore, the largest variances do not correspond to the meaningful axes, and the PCA-derived components may not have an obvious physical meaning. In this study, the CME was assumed statistically independent of other errors, and it was extracted using independent component analysis (ICA), which involves higher-order statistics. First, the ICA performance was tested using a simulated example and compared with PCA and stacking methods. The existence of strong local effects on some stations causes significant large spatial responses and, therefore, a strategy based on median and interquartile range statistics was proposed to identify abnormal sites. After discarding abnormal sites, two indices based on the analysis of the spatial responses of all sites in each independent component (east, north, and vertical) were used to define the CME quantitatively. Continuous GPS coordinate time series spanning ˜ 4.5 years obtained from 259 stations of the Tectonic and Environmental Observation Network of Mainland China (CMONOC II) were analyzed using both PCA and ICA methods and their results compared. The results suggest that PCA is susceptible to deriving an artificial spatial structure, whereas ICA separates the CME from other errors reliably. Our results demonstrate that the spatial characteristics of the CME for CMONOC II are not uniform for the east, north, and vertical components, but have an obvious north-south or east-west distribution. After discarding 84 abnormal sites and performing spatiotemporal

  17. A unified framework for group independent component analysis for multi-subject fMRI data.

    Science.gov (United States)

    Guo, Ying; Pagnoni, Giuseppe

    2008-09-01

    Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140-151.] and tensor PICA [Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294-311.], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio

  18. 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.

  19. Energy-efficient FastICA implementation for biomedical signal separation.

    Science.gov (United States)

    Van, Lan-Da; Wu, Di-You; Chen, Chien-Shiun

    2011-11-01

    This paper presents an energy-efficient fast independent component analysis (FastICA) implementation with an early determination scheme for eight-channel electroencephalogram (EEG) signal separation. The main contributions are as follows: (1) energy-efficient FastICA using the proposed early determination scheme and the corresponding architecture; (2) cost-effective FastICA using the proposed preprocessing unit architecture with one coordinate rotation digital computer-based eigenvalue decomposition processor and the proposed one-unit architecture with the hardware reuse scheme; and (3) low-computation-time FastICA using the four parallel one-units architecture. The resulting power dissipation of the FastICA implementation for eight-channel EEG signal separation is 16.35 mW at 100 MHz at 1.0 V. Compared with the design without early determination, the proposed FastICA architecture implemented in united microelectronics corporation 90 nm 1P9M complementary metal-oxide-semiconductor process with a core area of 1.221 × 1.218 mm2 can achieve average energy reduction by 47.63%. From the post-layout simulation results, the maximum computation time is 0.29 s.

  20. Independent component analysis for detection of condition changes in large diesels

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan; Fog, Torben L.

    2003-01-01

    Automatic detection and classification of operation conditions in large diesel engines is of significant importance. This paper investigates an independent component analysis (ICA) framework for unsupervised detection of changes in and possibly classification of operation conditions...... such as lubrication changes and increased wear based on acoustical emission (AE) sensor signals. The probabilistic formulation of ICA enables a statistical detection of novel events which do not conform to the current ICA model, thus indicating significant changes in operation conditions. Novelty of an observation...... is measured through the likelihood that the model has produced that observation. Evaluation of likelihood ratios allows the framework to also handle multiple models, thus enabling classification of operation conditions; furthermore the likelihood also serves as a link to traditional change detection...

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

    Institute of Scientific and Technical Information of China (English)

    CHEN Hong-Bin; FENG Jiu-Chao; FANG Yong

    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.

  2. 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...

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

    Directory of Open Access Journals (Sweden)

    Lee Dong-Sup

    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: instability 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 validate 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.

  4. Recursive principal components analysis.

    Science.gov (United States)

    Voegtlin, Thomas

    2005-10-01

    A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by the network are adapted to the temporal statistics of the input. Moreover, sequences stored in the network may be retrieved explicitly, in the reverse order of presentation, thus providing a straight-forward neural implementation of a logical stack.

  5. Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection

    Directory of Open Access Journals (Sweden)

    Hurdal Monica K

    2005-01-01

    Full Text Available Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA are considered to be hypothesis-generating procedures and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI. Recently, a new paradigm in ICA emerged, that of finding "clusters" of dependent components. This intriguing idea found its implementation into two new ICA algorithms: tree-dependent and topographic ICA. For fMRI, this represents the unifying paradigm of combining two powerful exploratory data analysis methods, ICA and unsupervised clustering techniques. For the fMRI data, a comparative quantitative evaluation between the two methods, tree-dependent and topographic ICA, was performed. The comparative results were evaluated by (1 task-related activation maps, (2 associated time courses, and (3 ROC study. The most important findings in this paper are that (1 both tree-dependent and topographic ICA are able to identify signal components with high correlation to the fMRI stimulus, and that (2 topographic ICA outperforms all other ICA methods including tree-dependent ICA for 8 and 9 ICs. However for 16 ICs, topographic ICA is outperformed by tree-dependent ICA (KGV using as an approximation of the mutual information the kernel generalized variance. The applicability of the new algorithm is demonstrated on experimental data.

  6. A post-modification approach to independent compo-nent analysis for resolution of overlapping GC/MS signals:from independent components to chemical components

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Independent component analysis (ICA) has demonstrated its power to extract mass spectra from over-lapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is pro-posed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.

  7. Estimation of individual evoked potential components using iterative independent component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zouridakis, G; Iyer, D; Diaz, J; Patidar, U [Department of Computer Science, University of Houston, 501 Philip G Hoffman Hall, Houston, TX 77204-3010 (United States)

    2007-09-07

    Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.

  8. 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.

  9. Parallel group independent component analysis for massive fMRI data sets

    Science.gov (United States)

    Huang, Lei; Qiu, Huitong; Nebel, Mary Beth; Mostofsky, Stewart H.; Pekar, James J.; Lindquist, Martin A.; Eloyan, Ani; Caffo, Brian S.

    2017-01-01

    Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively. PMID:28278208

  10. Parallel group independent component analysis for massive fMRI data sets.

    Science.gov (United States)

    Chen, Shaojie; Huang, Lei; Qiu, Huitong; Nebel, Mary Beth; Mostofsky, Stewart H; Pekar, James J; Lindquist, Martin A; Eloyan, Ani; Caffo, Brian S

    2017-01-01

    Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.

  11. Least Dependent Component Analysis Based on Mutual Information

    CERN Document Server

    Stögbauer, H; Astakhov, S A; Grassberger, P; St\\"ogbauer, Harald; Kraskov, Alexander; Astakhov, Sergey A.; Grassberger, Peter

    2004-01-01

    We propose to use precise estimators of mutual information (MI) to find least dependent components in a linearly mixed signal. On the one hand this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand it has the advantage, compared to other implementations of `independent' component analysis (ICA) some of which are based on crude approximations for MI, that the numerical values of the MI can be used for: (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output, by comparing the pairwise MIs with those of re-mixed components; (iii) clustering the output according to the residual interdependencies. For the MI estimator we use a recently proposed k-nearest neighbor based algorithm. For time sequences we combine this with delay embedding, in order to take into account non-trivial time correlations. After several tests with artificial data, we apply the resulting MILCA (Mutual Information based ...

  12. Towards Nonstationary, Nonparametric Independent Process Analysis with Unknown Source Component Dimensions

    CERN Document Server

    Szabo, Zoltan

    2010-01-01

    The goal of this paper is to extend independent subspace analysis (ISA) to the case of (i) nonparametric, not strictly stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) processes to model the temporal evolution of the hidden sources. An extension of the ISA separation principle--which states that the ISA problem can be solved by traditional independent component analysis (ICA) and clustering of the ICA elements--is derived for the solution of the defined fAR independent process analysis task (fAR-IPA): applying fAR identification we reduce the problem to ISA. A local averaging approach, the Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We extend the Amari-index to different dimensional components and illustrate the efficiency of the fAR-IPA approach by numerical examples.

  13. 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.

  14. 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.

  15. Local Component Analysis

    CERN Document Server

    Roux, Nicolas Le

    2011-01-01

    Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.e., a scalar multiplicative factor). In this paper, we propose to learn a full Euclidean metric through an expectation-minimization (EM) procedure, which can be seen as an unsupervised counterpart to neighbourhood component analysis (NCA). In order to avoid overfitting with a fully nonparametric density estimator in high dimensions, we also consider a semi-parametric Gaussian-Parzen density model, where some of the variables are modelled through a jointly Gaussian density, while others are modelled through Parzen windows. For these two models, EM leads to simple closed-form updates based on matrix inversions and eigenvalue decompositions. We show empirically that our method leads to density esti...

  16. Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data.

    Science.gov (United States)

    Du, Yuhui; Lin, Dongdong; Yu, Qingbao; Sui, Jing; Chen, Jiayu; Rachakonda, Srinivas; Adali, Tulay; Calhoun, Vince D

    2017-01-01

    Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks

  17. Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.

    Science.gov (United States)

    Pruim, Raimon H R; Mennes, Maarten; Buitelaar, Jan K; Beckmann, Christian F

    2015-05-15

    We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes data-driven derived components that represent motion-related artifacts. Here we present an extensive evaluation of ICA-AROMA by comparing our strategy to a range of alternative strategies for motion-related artifact removal: (i) no secondary motion correction, (ii) extensive nuisance regression utilizing 6 or (iii) 24 realignment parameters, (iv) spike regression (Satterthwaite et al., 2013a), (v) motion scrubbing (Power et al., 2012), (vi) aCompCor (Behzadi et al., 2007; Muschelli et al., 2014), (vii) SOCK (Bhaganagarapu et al., 2013), and (viii) ICA-FIX (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014), without re-training the classifier. Using three different functional connectivity analysis approaches and four different multi-subject resting-state fMRI datasets, we assessed all strategies regarding their potential to remove motion artifacts, ability to preserve signal of interest, and induced loss in temporal degrees of freedom (tDoF). Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets. These results demonstrate that ICA-AROMA is an effective strategy for removing motion-related artifacts from rfMRI data. Our robust and generalizable strategy avoids the need for censoring fMRI data and reduces motion-induced signal variations in fMRI data, while preserving signal of interest and increasing the reproducibility of functional connectivity metrics. In addition, ICA-AROMA preserves the temporal non

  18. 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

  19. 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.

  20. 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.

  1. 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

    2017-07-04

    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 (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) 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 pre-trained 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 multi-channel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

  2. Dependent component analysis based approach to robust demarcation of skin tumors

    Science.gov (United States)

    Kopriva, Ivica; Peršin, Antun; Puizina-Ivić, Neira; Mirić, Lina

    2009-02-01

    Method for robust demarcation of the basal cell carcinoma (BCC) is presented employing novel dependent component analysis (DCA)-based approach to unsupervised segmentation of the red-green-blue (RGB) fluorescent image of the BCC. It exploits spectral diversity between the BCC and the surrounding tissue. DCA represents an extension of the independent component analysis (ICA) and is necessary to account for statistical dependence induced by spectral similarity between the BCC and surrounding tissue. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization and ICA we experimentally demonstrate good performance of DCA-based BCC demarcation in demanding scenario where intensity of the fluorescent image has been varied almost two-orders of magnitude.

  3. OLED Defect Inspection System Development through Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2012-12-01

    Full Text Available Organic Light Emitting Displays (OLED is a new type of display device which has become increasingly attractive and popular. Due to the complex manufacturing process, various defects may exist on the OLED panel affecting the quality and life of the display panels. These defects have the characteristics of fuzzy boundaries, irregular in shape, low contrast with background and especially, they are mixed with the pixel texture background increasing the difficulty of a rapid recognition. In this paper, we proposed an approach to detect the defects based on the model of independent component analysis (ICA. The ICA model is applied to a perfect OLED image to estimate its corresponding independent components (ICs and create the de-mixing matrix. Through estimation and determination of a proper ICi row vector of the faultless image, a new de-mixing matrix can be generated which constitutes only uniform information and is then applied to reconstruct the texture background of source OLED images. Through the operation of subtraction of the reconstructed background from the source images and the binary segmentation, the defects can be detected. Based on the algorithms, a defect detection system for the OLED panels was implemented and the testing work was performed in this study. The testing results show that the proposed method is feasible and effective.

  4. 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...

  5. 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...

  6. Analysis Components Investigation Report

    Science.gov (United States)

    2014-10-01

    value is t ds each te rms presen t, and !()*+) PREVIOUS WRI open sour a training , tagged -1 t or... measure tion and Analys 2 is Component THIS DOCU The mis pro inte cou For sele con The use as solu ran .3 Prot In t be A G and wh and sec Wh info Thi...ASSIFIED December 2 LOSED TO ANY P d to specif c. The valu , the user c keywords p as relevan ument’s me system cou the docum iterion and/ e

  7. Facies recognition using a smoothing process through Fast Independent Component Analysis and Discrete Cosine Transform

    Science.gov (United States)

    Sanchetta, Alexandre Cruz; Leite, Emilson Pereira; Honório, Bruno César Zanardo

    2013-08-01

    We propose a preprocessing methodology for well-log geophysical data based on Fast Independent Component Analysis (FastICA) and Discrete Cosine Transform (DCT), in order to improve the success rate of the K-NN automatic classifier. The K-NN have been commonly applied to facies recognition in well-log geophysical data for hydrocarbon reservoir modeling and characterization. The preprocess was made in two different levels. In the first level, a FastICA based dimenstion reduction was applied, maintaining much of the information, and its results were classified; In second level, FastICA and DCT were applied in smoothing level, where the data points are modified, so individual points have their distance reduced, keeping just the primordial information. The results were compared to identify the best classification cases. We have applied the proposed methodology to well-log data from a petroleum field of Campos Basin, Brazil. Sonic, gamma-ray, density, neutron porosity and deep induction logs were preprocessed with FastICA and DCT, and the product was classified with K-NN. The success rates in recognition were calculated by appling the method to log intervals where core data were available. The results were compared to those of automatic recognition of the original well-log data set with and without the removal of high frequency noise. We conclude that the application of the proposed methodology significantly improves the success rate of facies recognition by K-NN.

  8. 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.

  9. Gold price analysis based on ensemble empirical model decomposition and independent component analysis

    Science.gov (United States)

    Xian, Lu; He, Kaijian; Lai, Kin Keung

    2016-07-01

    In recent years, the increasing level of volatility of the gold price has received the increasing level of attention from the academia and industry alike. Due to the complexity and significant fluctuations observed in the gold market, however, most of current approaches have failed to produce robust and consistent modeling and forecasting results. Ensemble Empirical Model Decomposition (EEMD) and Independent Component Analysis (ICA) are novel data analysis methods that can deal with nonlinear and non-stationary time series. This study introduces a new methodology which combines the two methods and applies it to gold price analysis. This includes three steps: firstly, the original gold price series is decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. Secondly, IMFs are further processed with unimportant ones re-grouped. Then a new set of data called Virtual Intrinsic Mode Functions (VIMFs) is reconstructed. Finally, ICA is used to decompose VIMFs into statistically Independent Components (ICs). The decomposition results reveal that the gold price series can be represented by the linear combination of ICs. Furthermore, the economic meanings of ICs are analyzed and discussed in detail, according to the change trend and ICs' transformation coefficients. The analyses not only explain the inner driving factors and their impacts but also conduct in-depth analysis on how these factors affect gold price. At the same time, regression analysis has been conducted to verify our analysis. Results from the empirical studies in the gold markets show that the EEMD-ICA serve as an effective technique for gold price analysis from a new perspective.

  10. Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.

  11. JFDE Special ICAE 2015

    Directory of Open Access Journals (Sweden)

    Tillmann Klein

    2015-06-01

    Full Text Available We are proud to announce that the Journal of Facade Design and Engineering is becoming a firm partner for the distribution of scientific knowledge of the ICAE International Congress on Architectural Envelopes, organised by Tecnalia San Sebastian. Tecnalia is one of the founding members of the European Facade Network EFN, and this partnership supports the development of JFDE with regards to the discipline of facade design and engineering. This issue of JFDE is dedicated to ICAE 2015, the VIIth edition of the congress. The contributions have been carefully selected from 32 abstracts, submitted to the scientific section of the conference. Subsequently the finished papers have been subjected to the regular blind review process of the journal. At this point, we want to thank our special editors Julen Astudillo and Jose Antonio Chica for their effort to make this partnership happen. The paper contributions show an interesting selection of approaches to innovative materials, form finding, simulation and climatic concepts. This demonstrates the special character of the discipline we are working in, bridging research, design and practice. Facade Design and Engineering is a peer reviewed, open access journal, funded by The Netherlands Organisation for Scientific Research NWO (www.nwo.nl. We see ‘open access’ as the future publishing model. But it certainly requires new financial models which we will have to explore over the coming years.

  12. Shifted Independent Component Analysis

    DEFF Research Database (Denmark)

    Mørup, Morten; Madsen, Kristoffer Hougaard; Hansen, Lars Kai

    2007-01-01

    Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and financial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carrie...

  13. Joint independent component analysis of structural and functional images reveals complex patterns of functional reorganisation in stroke aphasia.

    Science.gov (United States)

    Specht, Karsten; Zahn, Roland; Willmes, Klaus; Weis, Susanne; Holtel, Christiane; Krause, Bernd J; Herzog, Hans; Huber, Walter

    2009-10-01

    Previous functional activation studies in patients with aphasia have mostly relied on standard group comparisons of aphasic patients with healthy controls, which are biased towards regions showing the most consistent effects and disregard variability within groups. Groups of aphasic patients, however, show considerable variability with respect to lesion localisation and extent. Here, we use a novel method, joint independent component analysis (jICA), which allowed us to investigate abnormal patterns of functional activation with O(15)-PET during lexical decision in aphasic patients after middle cerebral artery stroke and to directly relate them to lesion information from structural MRI. Our results demonstrate that with jICA we could detect a network of compensatory increases in activity within bilateral anterior inferior temporal areas (BA20), which was not revealed by standard group comparisons. In addition, both types of analyses, jICA and group comparison, showed increased activity in the right posterior superior temporal gyrus in aphasic patients. Further, whereas standard analyses revealed no decreases in activation, jICA identified that left perisylvian lesions were associated with decreased activation of left posterior inferior frontal cortex, damaged in most patients, and extralesional remote decreases of activity within polar parts of the inferior temporal gyrus (BA38/20) and the occipital cortex (BA19). Taken together, our results demonstrate that jICA may be superior in revealing complex patterns of functional reorganisation in aphasia.

  14. Nonlinear independent component analysis: Existence and uniqueness results.

    Science.gov (United States)

    Hyvärinen, Aapo; Pajunen, Petteri

    1999-04-01

    The question of existence and uniqueness of solutions for nonlinear independent component analysis is addressed. It is shown that if the space of mixing functions is not limited there exists always an infinity of solutions. In particular, it is shown how to construct parameterized families of solutions. The indeterminacies involved are not trivial, as in the linear case. Next, it is shown how to utilize some results of complex analysis to obtain uniqueness of solutions. We show that for two dimensions, the solution is unique up to a rotation, if the mixing function is constrained to be a conformal mapping together with some other assumptions. We also conjecture that the solution is strictly unique except in some degenerate cases, as the indeterminacy implied by the rotation is essentially similar to estimating the model of linear ICA.

  15. Structure Learning by Pruning in Independent Component Analysis

    DEFF Research Database (Denmark)

    Kjems, Andreas; Hansen, Lars Kai

    2006-01-01

    We discuss pruning as a means of structure learning in independent component analysis. Sparse models are attractive in both signal processing and in analysis of abstract data, they can assist model interpretation, generalizability and reduce computation. We derive the relevant saliency expression...... methods, for both small and large samples. The Bayesian information criterion (BIC) seems to outperform both AIC and test sets as tools for determining the optimal degree of sparsity....... and compare with magnitude based pruning and Bayesian sparsification. We show in simulations that pruning is able to identify underlying sparse structures without prior knowledge on the degree of sparsity. We find that for ICA magnitude based pruning is as efficient as saliency based methods and Bayesian...

  16. Impact of automated ICA-based denoising of fMRI data in acute stroke patients.

    Science.gov (United States)

    Carone, D; Licenik, R; Suri, S; Griffanti, L; Filippini, N; Kennedy, J

    2017-01-01

    Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow focused strategies to clean the data enough and the heterogeneity in the data may be so great to critically undermine complex approaches. The purpose of this study was to explore what automated ICA based approach would better cope with these limitations when cleaning fMRI data obtained from acute stroke patients. The performance of a focused classifier (ICA-AROMA) and a complex classifier (FIX) approaches were compared using data obtained from twenty consecutive acute lacunar stroke patients using metrics determining RSN identification, RSN reproducibility, changes in the BOLD variance, differences in the estimation of functional connectivity and loss of temporal degrees of freedom. The use of generic-trained FIX resulted in misclassification of components and significant loss of signal (< 80%), and was not explored further. Both ICA-AROMA and patient-trained FIX based denoising approaches resulted in significantly improved RSN reproducibility (p < 0.001), localized reduction in BOLD variance consistent with noise removal, and significant changes in functional connectivity (p < 0.001). Patient-trained FIX resulted in higher RSN identifiability (p < 0.001) and wider changes both in the BOLD variance and in functional connectivity compared to ICA-AROMA. The success of ICA-AROMA suggests that by focusing on selected components the full automation can deliver meaningful data for analysis even in population with multiple sources of noise. However, the time invested to train FIX

  17. Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis.

    Science.gov (United States)

    Wessel, Jan R

    2016-03-08

    Temporal independent component analysis (ICA) is applied to an electrophysiological signal mixture (such as an EEG recording) to disentangle the independent neural source signals-independent components-underlying said signal mixture. When applied to scalp EEG, ICA is most commonly used either as a pre-processing step (e.g., to isolate physiological processes from non-physiological artifacts), or as a data-reduction step (i.e., to focus on one specific neural process with increased signal-to-noise ratio). However, ICA can be used in an even more powerful way that fundamentally expands the inferential utility of scalp EEG. The core assumption of EEG-ICA-namely, that individual independent components represent separable neural processes-can be leveraged to derive the following inferential logic: If a specific independent component shows activity related to multiple psychological processes within the same dataset (e.g., elicited by different experimental events), it follows that those psychological processes involve a common, non-separable neural mechanism. As such, this logic allows testing a class of hypotheses that is beyond the reach of regular EEG analyses techniques, thereby crucially increasing the inferential utility of the EEG. In the current article, this logic will be referred to as the 'common independent process identification' (CIPI) approach. This article aims to provide a tutorial into the application of this powerful approach, targeted at researchers that have a basic understanding of standard EEG analysis. Furthermore, the article aims to exemplify the usage of CIPI by outlining recent studies that successfully applied this approach to test neural theories of mental functions.

  18. Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials.

    Science.gov (United States)

    Cong, Fengyu; Leppänen, Paavo H T; Astikainen, Piia; Hämäläinen, Jarmo; Hietanen, Jari K; Ristaniemi, Tapani

    2011-09-30

    The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the number of sources) simultaneously. We demonstrated these effects using two datasets, one containing visual and the other auditory ERPs. The results showed that the method including OF and ICA extracted much more reliable components than the sole ICA without OF did, and that OF removed some non-targeted sources and made the underdetermined model of EEG recordings approach to the determined one. Thus, we suggest designing an OF based on the properties of an ERP to filter recordings before using ICA decomposition to extract the targeted ERP component. Copyright © 2011 Elsevier B.V. All rights reserved.

  19. Multiview Bayesian Correlated Component Analysis

    DEFF Research Database (Denmark)

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

    2015-01-01

    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....... 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...

  20. ICA Based Speckle Filtering for Target Extraction in SAR Images Using Adaptive Space Separation

    Institute of Scientific and Technical Information of China (English)

    LI Yu-tong; ZHOU Yue; YANG Lei

    2008-01-01

    A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.

  1. An ICA with reference approach in identification of genetic variation and associated brain networks

    Directory of Open Access Journals (Sweden)

    Jingyu eLiu

    2012-02-01

    Full Text Available To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients and 40 healthy controls include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p<4×10-17, and discovered a brain functional network that was significantly associated with this genetic component (p<1×10-4. The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain.

  2. Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

    Science.gov (United States)

    Moosmann, Matthias; Eichele, Tom; Nordby, Helge; Hugdahl, Kenneth; Calhoun, Vince D

    2008-03-01

    An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

  3. Regularized Generalized Structured Component Analysis

    Science.gov (United States)

    Hwang, Heungsun

    2009-01-01

    Generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge…

  4. Analyzing the pupil response due to increased cognitive demand: an independent component analysis study.

    Science.gov (United States)

    Jainta, S; Baccino, T

    2010-07-01

    Pupillometry is used to indicate the relative extent of processing demands within or between tasks; however, this analysis is complicated by the fact that the pupil also responds to low-level aspects of visual input. First, we attempted to identify "principal" components that contribute to the pupil response by computing a principal component analysis (PCA) and second, to reveal "hidden" sources within the pupil response by calculating an independent component analysis (ICA). Pupil response data were collected while subjects read, added or multiplied numbers. A set of 3 factors/components were identified as resembling the individual pupil responses, but only one ICA component changed in concordance to the cognitive demand. This component alone accounted for about 50% of the variance of the pupil response during the most demanding task, i.e. the multiplication task. The highest impact of this factor was observed for 2000 to 300ms after task onset. Even though we did not attempt to answer the question of the functional background of the components 1 and 3, we speculated that component 2 might reflect the effort a subject engages to perform a task with greater difficulty.

  5. Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

    Science.gov (United States)

    Mishra, Puneet; Singla, Sunil Kumar

    2013-01-01

    In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

  6. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering

    Directory of Open Access Journals (Sweden)

    Yao-Tien Chen

    2017-01-01

    Full Text Available Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.

  7. 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 are succes...

  8. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    Science.gov (United States)

    Uddin, Md; Lee, J J; Kim, T S

    2008-01-01

    In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

  9. Military target detection using spectrally modeled algorithms and independent component analysis

    Science.gov (United States)

    Tiwari, Kailash Chandra; Arora, Manoj K.; Singh, Dharmendra; Yadav, Deepti

    2013-02-01

    Most military targets of strategic importance are very small in size. Though some of them may get spatially resolved, most cannot be detected due to lack of adequate spectral resolution. Hyperspectral data, acquired over hundreds of narrow contiguous wavelength bands, are extremely suitable for most military target detection applications. Target detection, however, still remains complicated due to a host of other issues. These include, first, the heavy volume of hyperspectral data, which leads to computational complexities; second, most materials in nature exhibit spectral variability and remain unpredictable; and third, most target detection algorithms are based on spectral modeling and availability of a priori target spectra is an essential requirement, a condition difficult to meet in practice. Independent component analysis (ICA) is a new evolving technique that aims at finding components that are statistically independent or as independent as possible. It does not have any requirement of a priori availability of target spectra and is an attractive alternative. This paper, presents a study of military target detection using four spectral matching algorithms, namely, orthogonal subspace projection (OSP), constrained energy minimisation, spectral angle mapper and spectral correlation mapper, four anomaly detection algorithms, namely, OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), uniform target detector (UTD), a combination of RXD-UTD. The performances of these spectrally modeled algorithms are then also compared with ICA using receiver operating characteristic analysis. The superior performance of ICA indicates that it may be considered a viable alternative for military target detection.

  10. ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks

    Directory of Open Access Journals (Sweden)

    Siddharth eKhullar

    2011-11-01

    Full Text Available A common pre-processing challenge associated with group-level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute (MNI brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting-state group ICA networks. We posit that these intrinsic networks can provide to the spatial normalization process with important information about how each individual’s brain is organized functionally. The algorithm is initiated by the extraction of single-subject representations of intrinsic networks using group level independent component analysis (ICA on resting state fMRI data. In this proof of concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant intrinsic networks are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods¬: the general linear model (GLM and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics

  11. ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks.

    Science.gov (United States)

    Khullar, Siddharth; Michael, Andrew M; Cahill, Nathan D; Kiehl, Kent A; Pearlson, Godfrey; Baum, Stefi A; Calhoun, Vince D

    2011-01-01

    A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual's brain is organized functionally. The algorithm is initiated by the extraction of single subject representations of INs using group level independent component analysis (ICA) on resting state fMRI data. In this proof-of-concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant INs are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods: the general linear model and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics of task-fMRI data.

  12. Features of spatiotemporal groundwater head variation using independent component analysis

    Science.gov (United States)

    Hsiao, Chin-Tsai; Chang, Liang-Cheng; Tsai, Jui-Pin; Chen, You-Cheng

    2017-04-01

    The effect of external stimuli on a groundwater system can be understood by examining the features of spatiotemporal head variations. However, the head variations caused by various external stimuli are mixed signals. To identify the stimuli features of head variations, we propose a systematic approach based on independent component analysis (ICA), frequency analysis, cross-correlation analysis, well-selection strategy, and hourly average head analysis. We also removed the head variations caused by regional stimuli (e.g., rainfall and river stage) from the original head variations of all the wells to better characterize the local stimuli features (e.g., pumping and tide). In the synthetic case study, the derived independent component (IC) features are more consistent with the features of the given recharge and pumping than the features derived from principle component analysis. In a real case study, the ICs associated with regional stimuli highly correlated with field observations, and the effect of regional stimuli on the head variation of all the wells was quantified. In addition, the tide, agricultural, industrial, and spring pumping features were characterized. Therefore, the developed method can facilitate understanding of the features of the spatiotemporal head variation and quantification of the effects of external stimuli on a groundwater system.

  13. Cocaine addiction related reproducible brain regions of abnormal default-mode network functional connectivity: a group ICA study with different model orders.

    Science.gov (United States)

    Ding, Xiaoyu; Lee, Seong-Whan

    2013-08-26

    Model order selection in group independent component analysis (ICA) has a significant effect on the obtained components. This study investigated the reproducible brain regions of abnormal default-mode network (DMN) functional connectivity related with cocaine addiction through different model order settings in group ICA. Resting-state fMRI data from 24 cocaine addicts and 24 healthy controls were temporally concatenated and processed by group ICA using model orders of 10, 20, 30, 40, and 50, respectively. For each model order, the group ICA approach was repeated 100 times using the ICASSO toolbox and after clustering the obtained components, centrotype-based anterior and posterior DMN components were selected for further analysis. Individual DMN components were obtained through back-reconstruction and converted to z-score maps. A whole brain mixed effects factorial ANOVA was performed to explore the differences in resting-state DMN functional connectivity between cocaine addicts and healthy controls. The hippocampus, which showed decreased functional connectivity in cocaine addicts for all the tested model orders, might be considered as a reproducible abnormal region in DMN associated with cocaine addiction. This finding suggests that using group ICA to examine the functional connectivity of the hippocampus in the resting-state DMN may provide an additional insight potentially relevant for cocaine-related diagnoses and treatments. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  14. [Fetal electrocardiogram extraction based on independent component analysis and quantum particle swarm optimizer algorithm].

    Science.gov (United States)

    Du, Yanqin; Huang, Hua

    2011-10-01

    Fetal electrocardiogram (FECG) is an objective index of the activities of fetal cardiac electrophysiology. The acquired FECG is interfered by maternal electrocardiogram (MECG). How to extract the fetus ECG quickly and effectively has become an important research topic. During the non-invasive FECG extraction algorithms, independent component analysis(ICA) algorithm is considered as the best method, but the existing algorithms of obtaining the decomposition of the convergence properties of the matrix do not work effectively. Quantum particle swarm optimization (QPSO) is an intelligent optimization algorithm converging in the global. In order to extract the FECG signal effectively and quickly, we propose a method combining ICA and QPSO. The results show that this approach can extract the useful signal more clearly and accurately than other non-invasive methods.

  15. 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.

  16. Image encryption based on Independent Component Analysis and Arnold’s Cat Map

    Directory of Open Access Journals (Sweden)

    Nidaa AbdulMohsin Abbas

    2016-03-01

    Full Text Available Security of the multimedia data including image and video is one of the basic requirements for the telecommunications and computer networks. In this paper, a new efficient image encryption technique is presented. It is based on modifying the mixing matrix in Independent Component Analysis (ICA using the chaotic Arnold’s Cat Map (ACM for encryption. First, the mixing matrix is generated from the ACM by insert square image of any dimension. Second, the mixing process is implemented using the mixing matrix and the image sources the result is the encryption images that depend on the number of sources. Third, images decrypted using ICA algorithms. We use the Joint Approximate Diagonalization of Eigen-matrices (JADE algorithm as a case study. The results of several experiments, PSNR, SDR and SSIM index tests compared with standard mixing matrix showed that the proposed image encryption system provided effective and safe way for image encryption.

  17. Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques

    CERN Document Server

    Bono, Valentina; Das, Saptarshi; Maharatna, Koushik

    2014-01-01

    In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and Independent Component Analysis (ICA). The signal cleaning performances of WPT-EMD and WPT-ICA algorithms have been compared using a signal-to-noise ratio (SNR)-like criterion for artifacts. The algorithms have been tested on multiple trials of four different artifact cases viz. eye-blinking and muscle artifacts including left and right hand movement and head-shaking.

  18. 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.

  19. Tracking non-stationary EEG sources using adaptive online recursive independent component analysis.

    Science.gov (United States)

    Hsu, Sheng-Hsiou; Pion-Tonachini, Luca; Jung, Tzyy-Ping; Cauwenberghs, Gert

    2015-01-01

    Electroencephalographic (EEG) source-level analyses such as independent component analysis (ICA) have uncovered features related to human cognitive functions or artifactual activities. Among these methods, Online Recursive ICA (ORICA) has been shown to achieve fast convergence in decomposing high-density EEG data for real-time applications. However, its adaptation performance has not been fully explored due to the difficulty in choosing an appropriate forgetting factor: the weight applied to new data in a recursive update which determines the trade-off between the adaptation capability and convergence quality. This study proposes an adaptive forgetting factor for ORICA (adaptive ORICA) to learn and adapt to non-stationarity in the EEG data. Using a realistically simulated non-stationary EEG dataset, we empirically show adaptive forgetting factors outperform other commonly-used non-adaptive rules when underlying source dynamics are changing. Standard offline ICA can only extract a subset of the changing sources while adaptive ORICA can recover all. Applied to actual EEG data recorded from a task-switching experiments, adaptive ORICA can learn and re-learn the task-related components as they change. With an adaptive forgetting factor, adaptive ORICA can track non-stationary EEG sources, opening many new online applications in brain-computer interfaces and in monitoring of brain dynamics.

  20. Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies

    Science.gov (United States)

    Zhao, Yang; Dai, Rui-Na; Xiao, Xiang; Zhang, Zong; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

    2017-02-01

    Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed "hyperscanning." Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

  1. Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica.

    Science.gov (United States)

    Lutter, D; Langmann, Th; Ugocsai, P; Moehle, C; Seibold, E; Splettstoesser, W D; Gruber, P; Lang, E W; Schmitz, G

    2009-08-01

    The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis tool, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNFalpha induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NFkappaB as one of the main regulatory genes during response to F. tularensis infection.

  2. Detection and Separation of Event-related Potentials from Multi-Artifacts Contaminated EEG by Means of Independent Component Analysis

    Institute of Scientific and Technical Information of China (English)

    WANGRong-chang; DUSi-dan; GAODun-tang

    2004-01-01

    Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto--adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.

  3. Independent Component Analysis of Complex Valued Signals Based on First-order Statistics

    Directory of Open Access Journals (Sweden)

    P.C. Xu

    2013-12-01

    Full Text Available This paper proposes a novel method based on first-order statistics, aims to solve the problem of the independent component extraction of complex valued signals in instantaneous linear mixtures. Single-step and iterative algorithms are proposed and discussed under the engineering practice. Theoretical performance analysis about asymptotic interference-to-signal ratio (ISR and probability of correct support estimation (PCE are accomplished. Simulation examples validate the theoretic analysis, and demonstrate that the single-step algorithm is extremely effective. Moreover, the iterative algorithm is more efficient than complex FastICA under certain circumstances.

  4. Repeatability of Spitzer/IRAC exoplanetary eclipses with Independent Component Analysis

    CERN Document Server

    Morello, Giuseppe; Tinetti, Giovanna

    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 $\\mu$m, have been taken with Spitzer. In some cases, in the past years, repeated observations and multiple reanalyses of the same datasets 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 IRAC data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-S/N cases. We describe the method and discuss the results obtained over twelve eclipses of the exoplanet XO...

  5. 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.

  6. Independent component analysis in the presence of noise in fMRI.

    Science.gov (United States)

    Cordes, Dietmar; Nandy, Rajesh

    2007-11-01

    A noisy version of independent component analysis (noisy ICA) is applied to simulated and real functional magnetic resonance imaging (fMRI) data. The noise covariance is explicitly modeled by an autoregressive (AR) model of order 1. The unmixing matrix of the data is determined using a variant of the FastICA algorithm based on Gaussian moments. The sources are estimated using the principle of maximum likelihood by modeling the source densities as asymmetric exponential functions. Effect of dimensionality reduction on the effective noise covariance used, accuracy of the obtained mixing matrix and degree of improvement in estimating fMRI sources are investigated. The primary conclusions after using this method of evaluation are as follows: (a) weighting matrix estimates are similar for noisy and conventional ICA in the realm of typical fMRI data, and (b) source estimates are improved by 5% (as measured by the correlation coefficient) in realistic simulated data by explicitly modeling the source densities and the noise, even when just a simple white noise model is used.

  7. Effects of repeatability measures on results of fMRI sICA: a study on simulated and real resting-state effects.

    Science.gov (United States)

    Remes, Jukka J; Starck, Tuomo; Nikkinen, Juha; Ollila, Esa; Beckmann, Christian F; Tervonen, Osmo; Kiviniemi, Vesa; Silven, Olli

    2011-05-15

    Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0.1 Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged. Copyright © 2010 Elsevier Inc. All rights reserved.

  8. 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.

  9. Fast Steerable Principal Component Analysis

    OpenAIRE

    Zhao, Zhizhen; Shkolnisky, Yoel; Singer, Amit

    2016-01-01

    Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2D images as large as a few hundred pixels in each direction. Here we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of two-dimensional images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of $n$ images of size $L \\times L$ pixels, the computational complexity of our a...

  10. Blind ICA detection based on second-order cone programming for MC-CDMA systems

    Science.gov (United States)

    Jen, Chih-Wei; Jou, Shyh-Jye

    2014-12-01

    The multicarrier code division multiple access (MC-CDMA) technique has received considerable interest for its potential application to future wireless communication systems due to its high data rate. A common problem regarding the blind multiuser detectors used in MC-CDMA systems is that they are extremely sensitive to the complex channel environment. Besides, the perturbation of colored noise may negatively affect the performance of the system. In this paper, a new coherent detection method will be proposed, which utilizes the modified fast independent component analysis (FastICA) algorithm, based on approximate negentropy maximization that is subject to the second-order cone programming (SOCP) constraint. The aim of the proposed coherent detection is to provide robustness against small-to-medium channel estimation mismatch (CEM) that may arise from channel frequency response estimation error in the MC-CDMA system, which is modulated by downlink binary phase-shift keying (BPSK) under colored noise. Noncoherent demodulation schemes are preferable to coherent demodulation schemes, as the latter are difficult to implement over time-varying fading channels. Differential phase-shift keying (DPSK) is therefore the natural choice for an alternative modulation scheme. Furthermore, the new blind differential SOCP-based ICA (SOCP-ICA) detection without channel estimation and compensation will be proposed to combat Doppler spread caused by time-varying fading channels in the DPSK-modulated MC-CDMA system under colored noise. In this paper, numerical simulations are used to illustrate the robustness of the proposed blind coherent SOCP-ICA detector against small-to-medium CEM and to emphasize the advantage of the blind differential SOCP-ICA detector in overcoming Doppler spread.

  11. Detection of experimental ERP effects in combined EEG-fMRI: evaluating the benefits of interleaved acquisition and independent component analysis.

    Science.gov (United States)

    Lavric, Aureliu; Bregadze, Nino; Benattayallah, Abdelmalek

    2011-02-01

    The present study examined the benefit of rapid alternation of EEG and fMRI (a common strategy for avoiding artifact caused by rapid switching of MRI gradients) for detecting experimental modulations of ERPs in combined EEG-fMRI. The study also assessed the advantages of aiding the extraction of specific ERP components by means of signal decomposition using Independent Component Analysis (ICA). 'Go-nogo' task stimuli were presented either during fMRI scanning or in the gaps between fMRI scans, resulting in 'gradient' and 'no-gradient' ERPs. 'Go-nogo' differences in the N2 and P3 components were subjected to conventional ERP analysis, as well as single-trial and reliability analyses. Comparable N2 and P3 enhancement on 'nogo' trials was found in the 'gradient' and 'no-gradient' ERPs. ICA-based signal decomposition resulted in better validity (as indicated by topography), greater stability and lower measurement error of the predicted ERP effects. While there was little or no benefit of acquiring ERPs in the gaps between fMRI scans, ICA decomposition did improve the detection of experimental ERP modulations. Simultaneous and continuous EEG-fMRI acquisition is preferable to interleaved protocols. ICA-based decomposition is useful not only for artifact cancellation, but also for the extraction of specific ERP components. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  12. 独立元分析方法(ICA)及其在化工过程监控和故障诊断中的应用%ICA AND ITS APPLICATION TO CHEMICAL PROCESS MONITORING AND FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    陈国金; 梁军; 钱积新

    2003-01-01

    Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes. However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied. In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non-Gaussian data from industry and improve the monitoring performance of process. In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes. Simulation result for a nonisothermal continuous stirred-tank reactor (CSTR) by the presented method verifies the effectiveness of ICA-based algorithm.

  13. Operational Modal Parameter Identification Based on ICA%基于ICA的工作模态参数识别

    Institute of Scientific and Technical Information of China (English)

    张锐; 黄晋英; 郎忠宝

    2015-01-01

    The paper expounds the principle of independent component analysis and the operational modal analysis based on the principle of ICA. The analysis demonstrates the consistency between ICA separation model and structural vibration modal analysis model. The ICA algorithm and the software OMA developed by Belgian LMS are applied to identify the condition of gear box and broken tooth through modal parameters respectively. This paper finds that the algorithm of ICA, comparing with Op.PolyMAX which is the most commonly used, has strong anti-noise performance. Besides, it is easy to operate and the identification is accurate, this kind of algorithm provides a new basis of work modal parameters identification.%本文分别阐述了独立分量分析和基于ICA的工作模态分析原理,发现了ICA分离模型与结构振动模态分析模型的一致性。应用ICA算法和比利时LMS公司的OMA分析软件分别对齿轮箱正常和断齿工况进行模态参数识别,对比发现,ICA算法与目前最常用的Op. PolyMAX算法相比抗噪性强,识别简便精准,为工作模态参数识别提供新的识别依据。

  14. 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.

  15. Detection of orange juice frauds using front-face fluorescence spectroscopy and Independent Components Analysis.

    Science.gov (United States)

    Ammari, Faten; Redjdal, Lamia; Rutledge, Douglas N

    2015-02-01

    The aim of this study was to find simple objective analytical methods to assess the adulteration of orange juice by grapefruit juice. The adulterations by addition of grapefruit juice were studied by 3D-front-face fluorescence spectroscopy followed by Independent Components Analysis (ICA) and by classical methods such as free radical scavenging activity and total flavonoid content. The results of this study clearly indicate that frauds by adding grapefruit juice to orange juice can be detected at percentages as low as 1%.

  16. The application of independent component analysis with projection method to two-task fMRI data over multiple subjects

    Science.gov (United States)

    Li, Rui; Hui, Mingqi; Yao, Li; Chen, Kewei; Long, Zhiying

    2011-03-01

    Spatial Independent component analysis (sICA) has been successfully used to analyze functional magnetic resonance (fMRI) data. However, the application of ICA was limited in multi-task fMRI data due to the potential spatial dependence between task-related components. Long et al. (2009) proposed ICA with linear projection (ICAp) method and demonstrated its capacity to solve the interaction among task-related components in multi-task fMRI data of single subject. However, it's unclear that how to perform ICAp over a group of subjects. In this study, we proposed a group analysis framework on multi-task fMRI data by combining ICAp with the temporal concatenation method reported by Calhoun (2001). The results of real fMRI experiment containing multiple visual processing tasks demonstrated the feasibility and effectiveness of the group ICAp method. Moreover, compared to the GLM method, the group ICAp method is more sensitive to detect the regions specific to each task.

  17. Speckle reduction of SAR images using ICA basis enhancement and separation

    Institute of Scientific and Technical Information of China (English)

    Yutong Li; Yue zhou

    2007-01-01

    @@ An approach for synthetic aperture radax (SAR) image de-noising based on independent component analysis (ICA) basis images is proposed. Firstly, the basis images and the code matrix of the original image are obtained using ICA algorithm. Then, pointwise H(o)lder exponent of each basis is computed as a cost criterion for basis enhancement, and then the enhanced basis images are classified into two sets according to a separation rule which separates the clean basis from the original basis. After these key procedures for speckle reduction, the clean image is finally obtained by reconstruction on the clean basis and original code matrix. The reconstructed image shows better visual perception and image quality compared with those obtained by other traditional techniques.

  18. Fast Steerable Principal Component Analysis.

    Science.gov (United States)

    Zhao, Zhizhen; Shkolnisky, Yoel; Singer, Amit

    2016-03-01

    Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Here, we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of 2-D images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of n images of size L × L pixels, the computational complexity of our algorithm is O(nL(3) + L(4)), while existing algorithms take O(nL(4)). The new algorithm computes the expansion coefficients of the images in a Fourier-Bessel basis efficiently using the nonuniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA.

  19. 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.

  20. Dorsal Hand Vein Biometry by Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    V.H.Yadav

    2012-07-01

    Full Text Available Biometric authentication provides a high security and reliable approach to be used in security access system. Personal identification based on hand vein patterns is a newly developed recent year. The pattern of blood veins in the hand is unique to every individual, even among identical twins, and it do notchange over time. These properties of uniqueness, stability and strong immunity to forgery of the vein patterns make it a potentially good biometric trait which offers greater security and reliable features for personal identification. In this study, we have used the BOSPHORUS hand vein database which has been taken under a source of NIR infrared radiation. For feature extraction we applied appearance based method ICA which produces independent components. To control over the number of independent component we preprocessed data by PCA before applying ICA, and gives good experimental results.

  1. Parametric functional principal component analysis.

    Science.gov (United States)

    Sang, Peijun; Wang, Liangliang; Cao, Jiguo

    2017-03-10

    Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). Most existing FPCA approaches use a set of flexible basis functions such as B-spline basis to represent the FPCs, and control the smoothness of the FPCs by adding roughness penalties. However, the flexible representations pose difficulties for users to understand and interpret the FPCs. In this article, we consider a variety of applications of FPCA and find that, in many situations, the shapes of top FPCs are simple enough to be approximated using simple parametric functions. We propose a parametric approach to estimate the top FPCs to enhance their interpretability for users. Our parametric approach can also circumvent the smoothing parameter selecting process in conventional nonparametric FPCA methods. In addition, our simulation study shows that the proposed parametric FPCA is more robust when outlier curves exist. The parametric FPCA method is demonstrated by analyzing several datasets from a variety of applications. © 2017, The International Biometric Society.

  2. EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods.

    Science.gov (United States)

    Lei, Xu; Valdes-Sosa, Pedro A; Yao, Dezhong

    2012-09-01

    Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

  3. 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.

  4. A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography.

    Science.gov (United States)

    Breuer, Lukas; Dammers, Jürgen; Roberts, Timothy P L; Shah, N Jon

    2014-02-01

    Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

  5. Sensitivity Analysis of Component Reliability

    Institute of Scientific and Technical Information of China (English)

    ZhenhuaGe

    2004-01-01

    In a system, Every component has its unique position within system and its unique failure characteristics. When a component's reliability is changed, its effect on system reliability is not equal. Component reliability sensitivity is a measure of effect on system reliability while a component's reliability is changed. In this paper, the definition and relative matrix of component reliability sensitivity is proposed, and some of their characteristics are analyzed. All these will help us to analyse or improve the system reliability.

  6. Interpretable functional principal component analysis.

    Science.gov (United States)

    Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

    2016-09-01

    Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data.

  7. Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

    Science.gov (United States)

    Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A.C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

    2017-01-01

    This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section. PMID:28327648

  8. Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

    Science.gov (United States)

    Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A. C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

    2017-03-01

    This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section.

  9. Signal Detection using ICA: Application to Chat Room Topic Spotting

    DEFF Research Database (Denmark)

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

    2001-01-01

    of components is significant relative to a ``white noise'' null hypothesis. It was recently proposed to use the so-called Bayesian information criterion (BIC) approximation, for estimation of such probabilities of competing hypotheses. Here, we apply this approach to the understanding of chat. We show that ICA...

  10. 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

  11. Remote Sensing Image Fusion Using Ica and Optimized Wavelet Transform

    Science.gov (United States)

    Hnatushenko, V. V.; Vasyliev, V. V.

    2016-06-01

    In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

  12. 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

  13. Repeatability of Spitzer/IRAC Exoplanetary Eclipses with Independent Component Analysis

    Science.gov (United States)

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

    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

  14. 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...... hypotheses for ICA based on dynamic decorrelation. The probabilities are evaluated in the so-called Bayesian information criterion approximation, however, they are able to detect the content of dynamic components as efficiently as an unbiased test set estimator....

  15. 颈内动脉动脉瘤3D-DSA重建成像的构型分析%Configuration analysis of 3D-DSA reconstruction imagings of internal carotid artery (ICA)aneurysm

    Institute of Scientific and Technical Information of China (English)

    刘军; 王霞; 王浩洲; 王琳; 李吉贞; 张明然; 王莲

    2013-01-01

    目的 分析颈内动脉(ICA)动脉瘤在3D-DSA中的三维构型特点,探讨其指导临床的意义.方法 回顾性总结77例ICA动脉瘤患者3D-DSA的影像资料,对77例107个动脉瘤应用Syngo Inspace软件任务卡进行重建成像,显示其三维容积及三维形态结构,研究动脉瘤形态与ICA及其分支动脉开口的结构关系.结果 按ICA Bouthillier分段,动脉瘤在C4~C5段4个,C6~C7段(床突上段)103个.动脉瘤以类圆形鼓泡状形态自ICA凸起,其形态可分为单泡型74个(69.2%)、双泡型21个(19.6%)、多泡型12个(11.2%),双泡型及多泡型动脉瘤以圆泡连体的方式沿颈动脉轴线排列,不会横向排列.其中单泡型动脉瘤又分为单泡漏斗型、单泡水泡型、单泡窄颈型及单泡宽颈型.根据动脉瘤瘤颈开口与ICA及其分支动脉开口的关系,又可将动脉瘤分为分支无关型、分支相关型和分支泡上发出型,分别有51个、37个和19个.结论 根据3D-DSA可了解ICA动脉瘤的部位、形态结构,尤其是动脉瘤开口与ICA及其分支动脉开口的结构关系,对临床选择治疗方法,以及对治疗过程中容易产生的问题、术后疗效的判断都具有重要的指导意义.%Objective To study the three-dimensional configuration characteristic of internal carotid artery (ICA) aneurysms in 3D-DSA reconstruction imaging,and to discusses its guiding meaning for clinical teeatment of ICA aneurysm.Methods Retrospective analysis of 77 patients with ICA aneurysms.There were 107 aneurysms in 77 cases.Syngo Inspace software was used to reconstruct imaging to display three-dimensional volume and structure of the carotid artery aneurysm,and to study the space structure relationship of aneurysm with the ICA and branch artery.Results According to the Bouthillier segmentation of ICA.There were 4 aneurysms occurred in C4 ~ C5,and 103 in C6 ~ C7 (supraclinoidal segment).The aneurysms protrude from ICA with a series of circular bubble

  16. Interim Progress Report on the Application of an Independent Components Analysis-based Spectral Unmixing Algorithm to Beowulf Computers

    Science.gov (United States)

    Lemeshewsky, George

    2003-01-01

    This report describes work done to implement an independent-components-analysis (ICA) -based blind unmixing algorithm on the Eastern Region Geography (ERG) Beowulf computer cluster. It gives a brief description of blind spectral unmixing using ICA-based techniques and a preliminary example of unmixing results for Landsat-7 Thematic Mapper multispectral imagery using a recently reported1,2,3 unmixing algorithm. Also included are computer performance data. The final phase of this work, the actual implementation of the unmixing algorithm on the Beowulf cluster, was not completed this fiscal year and is addressed elsewhere. It is noted that study of this algorithm and its application to land-cover mapping will continue under another research project in the Land Remote Sensing theme into fiscal year 2004.

  17. A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

    Science.gov (United States)

    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. PMID:25045738

  18. A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Chi-Jie Lu

    2014-01-01

    Full Text Available 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.

  19. ICA-based muscle artefact correction of EEG data: what is muscle and what is brain? Comment on McMenamin et al.

    Science.gov (United States)

    Olbrich, Sebastian; Jödicke, Johannes; Sander, Christian; Himmerich, Hubertus; Hegerl, Ulrich

    2011-01-01

    Independent component analysis (ICA)-based muscle artefact correction has become a popular tool within electroencephalographic (EEG) research. As a comment on the article by McMenamin et al. (2010), we want to address three issues concerning the claimed lack of sensitivity and specificity of this method. The under- or overestimation of myogenic and neurogenic signals after ICA-based muscle artefact correction reported by McMenamin et al. might be explainable in part by a) insufficient temporal independence of myogenic and neurogenic components when exploring more than one condition, b) wrong classification of myogenic or neurogenic components by human raters and c) differences of neuronal mass activity during tensed or relaxed-muscle conditions. Our own data show only significant differences regarding intracortical alpha band EEG-source estimates for contrasts between clean EEG data and artificially contaminated EEG data at group-analysis level but not between clean data and data after ICA-based correction. ICA-based artefact correction already provides a powerful tool for muscle artefact rejection. More research is needed for determining reliable criteria to delineate myogenic from neurogenic components.

  20. Double precision nonlinear cell for fast independent component analysis algorithm

    Science.gov (United States)

    Jain, V. K.

    2006-05-01

    Several advanced algorithms in defense and security objectives require high-speed computation of nonlinear functions. These include detection, localization, and identification. Increasingly, such computations must be performed in double precision accuracy in real time. In this paper, we develop a significance-based interpolative approach to such evaluations for double precision arguments. It is shown that our approach requires only one major multiplication, which leads to a unified and fast, two-cycle, VLSI architecture for mantissa computations. In contrast, the traditional iterative computations require several cycles to converge and typically these computations vary a lot from one function to another. Moreover, when the evaluation pertains to a compound or concatenated function, the overall time required becomes the sum of the times required by the individual operations. For our approach, the time required remains two cycles even for such compound or concatenated functions. Very importantly, the paper develops a key formula for predicting and bounding the worst case arithmetic error. This new result enables the designer to quickly select the architectural parameters without the expensive and intolerably long simulations, while guaranteeing the desired accuracy. The specific application focus is the mapping of the Independent Component Analysis (ICA) technique to a coarse-grain parallel-processing architecture.

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

    OpenAIRE

    Karlsen, Brian; Larsen, Jan; Sørensen, Helge Bjarup Dissing; Jakobsen, Kaj Bjarne

    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 are successfully evaluated and compared on a real SF-GPR time-series. Field-test data are acquired using a monostatic S-band rectangular waveguide antenna.

  2. Adaptive blind separation of underdetermined mixtures based on sparse component analysis

    Institute of Scientific and Technical Information of China (English)

    YANG ZuYuan; HE ZhaoShui; XIE ShengLi; FU YuLi

    2008-01-01

    The independence priori is very often used in the conventional blind source sepa-ration (BSS). Naturally, independent component analysis (ICA) is also employed to perform BSS very often. However, ICA is difficult to use in some challenging cases, such as underdetermined BSS or blind separation of dependent sources. Recently, sparse component analysis (SCA) has attained much attention because it is theo-retically available for underdetermined BSS and even for blind dependent source separation sometimes. However, SCA has not been developed very sufficiently. Up to now, there are only few existing algorithms and they are also not perfect as well in practice. For example, although Lewicki-Sejnowski's natural gradient for SCA is superior to K-mean clustering, it is just an approximation without rigorously theo-retical basis. To overcome these problems, a new natural gradient formula is pro-posed in this paper. This formula is derived directly from the cost function of SCA through matrix theory. Mathematically, it is more rigorous. In addition, a new and robust adaptive BSS algorithm is developed based on the new natural gradient. Simulations illustrate that this natural gradient formula is more robust and reliable than Lewicki-Sejnowski's gradient.

  3. Independent component analysis of DTI data reveals white matter covariances in Alzheimer's disease

    Science.gov (United States)

    Ouyang, Xin; Sun, Xiaoyu; Guo, Ting; Sun, Qiaoyue; Chen, Kewei; Yao, Li; Wu, Xia; Guo, Xiaojuan

    2014-03-01

    Alzheimer's disease (AD) is a progressive neurodegenerative disease with the clinical symptom of the continuous deterioration of cognitive and memory functions. Multiple diffusion tensor imaging (DTI) indices such as fractional anisotropy (FA) and mean diffusivity (MD) can successfully explain the white matter damages in AD patients. However, most studies focused on the univariate measures (voxel-based analysis) to examine the differences between AD patients and normal controls (NCs). In this investigation, we applied a multivariate independent component analysis (ICA) to investigate the white matter covariances based on FA measurement from DTI data in 35 AD patients and 45 NCs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We found that six independent components (ICs) showed significant FA reductions in white matter covariances in AD compared with NC, including the genu and splenium of corpus callosum (IC-1 and IC-2), middle temporal gyral of temporal lobe (IC-3), sub-gyral of frontal lobe (IC-4 and IC-5) and sub-gyral of parietal lobe (IC-6). Our findings revealed covariant white matter loss in AD patients and suggest that the unsupervised data-driven ICA method is effective to explore the changes of FA in AD. This study assists us in understanding the mechanism of white matter covariant reductions in the development of AD.

  4. Segregation between the parietal memory network and the default mode network: effects of spatial smoothing and model order in ICA.

    Science.gov (United States)

    Hu, Yang; Wang, Jijun; Li, Chunbo; Wang, Yin-Shan; Yang, Zhi; Zuo, Xi-Nian

    2016-01-01

    A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

  5. [Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

    Science.gov (United States)

    Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

    2013-12-01

    The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

  6. Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties

    Science.gov (United States)

    Adali, Tülay; Levin-Schwartz, Yuri; Calhoun, Vince D.

    2015-01-01

    Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the datasets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA) generalizes ICA to multiple datasets by exploiting the statistical dependence across the datasets, and hence, as we discuss in this paper, provides an attractive solution to fusion of data from multiple datasets along with ICA. In this paper, we focus on two multivariate solutions for multi-modal data fusion that let multiple modalities fully interact for the estimation of underlying features that jointly report on all modalities. One solution is the Joint ICA model that has found wide application in medical imaging, and the second one is the the Transposed IVA model introduced here as a generalization of an approach based on multi-set canonical correlation analysis. In the discussion, we emphasize the role of diversity in the decompositions achieved by these two models, present their properties and implementation details to enable the user make informed decisions on the selection of a model along with its associated parameters. Discussions are supported by simulation results to help highlight the main issues in the implementation of these methods. PMID:26525830

  7. Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    Bang-hua YANG; Liang-fei HE; Lin LIN; Qian WANG

    2015-01-01

    Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs), a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage, independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact artifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

  8. Using independent component analysis to remove artifacts in visual cortex responses elicited by electrical stimulation of the optic nerve

    Science.gov (United States)

    Lu, Yiliang; Cao, Pengjia; Sun, Jingjing; Wang, Jing; Li, Liming; Ren, Qiushi; Chen, Yao; Chai, Xinyu

    2012-04-01

    In visual prosthesis research, electrically evoked potentials (EEPs) can be elicited by one or more biphasic current pulses delivered to the optic nerve (ON) through penetrating electrodes. Multi-channel EEPs recorded from the visual cortex usually contain large stimulus artifacts caused by instantaneous electrotonic current spread through the brain tissue. These stimulus artifacts contaminate the EEP waveform and often make subsequent analysis of the underlying neural responses difficult. This is particularly serious when investigating EEPs in response to electrical stimulation with long duration and multi-pulses. We applied independent component analysis (ICA) to remove these electrical stimulation-induced artifacts during the development of a visual prosthesis. Multi-channel signals were recorded from visual cortices of five rabbits in response to ON electrical stimulation with various stimulus parameters. ON action potentials were then blocked by lidocaine in order to acquire cortical potentials only including stimulus artifacts. Correlation analysis of reconstructed artifacts by ICA and artifacts recorded after blocking the ON indicates successful removal of artifacts from electrical stimulation by the ICA method. This technique has potential applications in studies designed to optimize the electrical stimulation parameters used by visual prostheses.

  9. 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

    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...... MHz- 3.0 GHz. The detection and clutter reduction approaches based on SICA are successfully evaluated on this SF-GPR dataset....

  10. 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

  11. Blind component separation in wavelet space. Application to CMB analysis

    OpenAIRE

    Delabrouille, J.; J. -L. Starck; J.-F. Cardoso; Moudden, Y.

    2004-01-01

    It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse ast...

  12. Using Independent Components Analysis to diminish the response of groundwater in borehole strainmeter

    Science.gov (United States)

    Chen, Chih-Yen; Hu, Jyr-Ching

    2017-04-01

    With designed feather, borehole strainmeter can not only record minor signals of tectonic movements, but also broad environmental signs such as barometry, rainfall and groundwater. Among these external factor, groundwater will influence the observation of borehole strainmeter mostly. According to essential observation, groundwater will cause much bigger response than the target tectonic strain change. We use co-sited piezometer to record pore pressure of groundwater in the rock formation in order to obtain the relationship of stain change and pore pressure. But there still exist some puzzle that can not be solved. First, due to instrument limitation, we could not set the pore pressure transducer in the same aquifer as strainmeter did. In this case, the response due to pore pressure change might be not fully correct. Furthermore, through pore-pressure transducers were set in most observatory, problem of electricity and connectivity will cause the record lack and lost. Therefore, it is necessary to find out a better and more stable method to diminish the groundwater response of strainmeter data.Strain transducer with different orientation can observe the groundwater response in different scale. If we can extract out groundwater signal from each independent strain transducer and estimate its original source. That will significantly rise signal strength and lower noise level. The case belongs some kind of blind-signal-separation (BSS) problem. The procedure of BSS extract or rebuild signal that can't be observed directly in many mixed sources and Independent-Component-Analysis (ICA) is one method adopted broadly. ICA is an analysis to find out parts which have statistics independence and non-Gaussian factor in complex signals. We use FastICA developed by to figure out the groundwater response strain in original strain data, and try to diminish it to rise the signal strength. We preceded strain data previously, then using ICA to separate data into serval independent

  13. 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.

  14. The Research on Wavelet Audio Watermark Based on Independent Component Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ma, X F [Engineering Training Centre, Harbin Engineering University, Harbin, 150001 (China); Jiang, T [Info. and Comm. Engineering College, Harbin Engineering University, Harbin, 150001 (China)

    2006-10-15

    Along with the development of the watermark technique many new scheme were presented and most of them were proved efficient. Some researchers have presented extraction of audio watermark using ICA in spatial domain. In this paper, we present a wavelet audio watermark using ICA. We embedded the image watermark into the wavelet coefficient of the audio signal, and extracted the watermark image using ICA in wavelet domain. We added noise on the watermark audio for analysis and the simulation results show that this watermark scheme we present is efficient and robustness.

  15. Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery.

    Science.gov (United States)

    Calhoun, Vince D; Adalı, Tülay

    2012-01-01

    Since the discovery of functional connectivity in fMRI data (i.e., temporal correlations between spatially distinct regions of the brain) there has been a considerable amount of work in this field. One important focus has been on the analysis of brain connectivity using the concept of networks instead of regions. Approximately ten years ago, two important research areas grew out of this concept. First, a network proposed to be "a default mode of brain function" since dubbed the default mode network was proposed by Raichle. Secondly, multisubject or group independent component analysis (ICA) provided a data-driven approach to study properties of brain networks, including the default mode network. In this paper, we provide a focused review of how ICA has contributed to the study of intrinsic networks. We discuss some methodological considerations for group ICA and highlight multiple analytic approaches for studying brain networks. We also show examples of some of the differences observed in the default mode and resting networks in the diseased brain. In summary, we are in exciting times and still just beginning to reap the benefits of the richness of functional brain networks as well as available analytic approaches.

  16. Spinal fMRI during proprioceptive and tactile tasks in healthy subjects: activity detected using cross-correlation, general linear model and independent component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Valsasina, P.; Agosta, F.; Filippi, M. [Scientific Institute Ospedale San Raffaele, Neuroimaging Research Unit, Milan (Italy); Caputo, D. [Scientific Institute Fondazione Don Gnocchi, Department of Neurology, Milan (Italy); Stroman, P.W. [Queen' s University, Department of Diagnostic Radiology, Centre for Neuroscience Studies, Kingston, ON (Canada)

    2008-10-15

    Functional MRI (fMRI) of the spinal cord is able to provide maps of neuronal activity. Spinal fMRI data have been analyzed in previous studies by calculating the cross-correlation (CC) between the stimulus and the time course of every voxel and, more recently, by using the general linear model (GLM). The aim of this study was to compare three different approaches (CC analysis, GLM and independent component analysis (ICA)) for analyzing fMRI scans of the cervical spinal cord. We analyzed spinal fMRI data from healthy subjects during a proprioceptive and a tactile stimulation by using two model-based approaches, i.e., CC analysis between the stimulus shape and the time course of every voxel, and the GLM. Moreover, we applied independent component analysis, a model-free approach which decomposes the data in a set of source signals. All methods were able to detect cervical cord areas of activity corresponding to the expected regions of neuronal activations. Model-based approaches (CC and GLM) revealed similar patterns of activity. ICA could identify a component correlated to fMRI stimulation, although with a lower statistical threshold than model-based approaches, and many components, consistent across subjects, which are likely to be secondary to noise present in the data. Model-based approaches seem to be more robust for estimating task-related activity, whereas ICA seems to be useful for eliminating noise components from the data. Combined use of ICA and GLM might improve the reliability of spinal fMRI results. (orig.)

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

    Science.gov (United States)

    Schelkanova, Irina; Toronov, Vladislav

    2011-07-01

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

  18. Assessment of an ICA-based noise reduction method for multi-channel auditory evoked potentials

    Science.gov (United States)

    Mirahmadizoghi, Siavash; Bell, Steven; Simpson, David

    2015-03-01

    In this work a new independent component analysis (ICA) based method for noise reduction in evoked potentials is evaluated on for auditory late responses (ALR) captured with a 63-channel electroencephalogram (EEG) from 10 normal-hearing subjects. The performance of the new method is compared with a single channel alternative in terms of signal to noise ratio (SNR), the number of channels with an SNR above an empirically derived statistical critical value and an estimate of hearing threshold. The results show that the multichannel signal processing method can significantly enhance the quality of the signal and also detected hearing thresholds significantly lower than with the single channel alternative.

  19. An ICA and EC based approach for blind equalization and channel parameter estimation

    Institute of Scientific and Technical Information of China (English)

    何振亚; 刘琚; 杨绿溪; 蔚承建

    2000-01-01

    A new on-line blind equalization approach is proposed. The approach combines over-sampling technique with independent component analysis (ICA) neural network and can give equalized output on-line employing only the received signal. Based on the fourth-order cumulants and the characteristic of the linear system, the parameters of original channel are also estimated using evolutionary computation (EC). Compared to traditional equalization methods, the proposed algorithm is of simple architecture, does not need learning sequences apart from the observation, and can achieve both blind equalization and system identification. Computer simulations show good performance.

  20. An ICA and EC based approach for blind equalization and channel parameter estimation

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A new on-line blind equalization approach is proposed. The approach combines over-sampling technique with independent component analysis (ICA)neural network and can give equalized output on-line employing only the received signal. Based on the fourth-order cumulants and the characteristic of the linear system, the parameters of original channel are also estimated using evolutionary computation(EC).Compared to traditional equalization methods, the proposed algorithm is of simple architecture, does not need learning sequences apart from the observation, and can achieve both blind equalization and system identification. Computer simulations show good performance.

  1. Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm.

    Science.gov (United States)

    Mangalathu-Arumana, J; Beardsley, S A; Liebenthal, E

    2012-05-01

    The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1-8 μV in the time window of the P300 (350-700 ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post-central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI. Copyright © 2012 Elsevier Inc. All rights reserved.

  2. 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.

  3. 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.

  4. A comparative and combined study of EMIS and GPR detectors by the use of independent component analysis

    Science.gov (United States)

    Morgenstjerne, Axel; Karlsen, Brian; Larsen, Jan; Sorensen, Helge B. D.; Jakobsen, Kaj B.

    2005-06-01

    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 detector is a GEM-3, which is a monostatic sensor measuring the response of the environment on a multi-frequency constant wave excitation field (300 Hz 25 kHz), and the GPR detector is a stepped-frequency GPR with a monostatic bow-tie antenna (500 MHz 2.5 GHz). For both sensors the in-phase and the quadrature responses are measured at each frequency. The test field is a box of soil where a wide range of UXOs are placed at selected positions. The position and movement of both of the detectors are controlled by a 2D-scanner. Thus the data are acquired at well-defined measurement points. The data are processed by the use of statistical signal processing based on ICA. An unsupervised method based on ICA to detect, discriminate, and classify the UXOs from clutter is suggested. The approach is studied on GPR and EMIS data, both separately and combined. The potential is an improved ability: to detect the UXOs, to evaluate the related characteristics, and to reduce the number of false alarms from harmless objects and clutter.

  5. 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.

  6. Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics.

    Science.gov (United States)

    Domingo-Almenara, Xavier; Perera, Alexandre; Ramírez, Noelia; Brezmes, Jesus

    2016-07-01

    Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R(2) coefficients of determination were in good agreement (R(2)>0.90) while up to 24 cases exhibited an excellent linear relation (R(2)>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS.

  7. Single-trial event-related potential extraction through one-unit ICA-with-reference

    Science.gov (United States)

    Lih Lee, Wee; Tan, Tele; Falkmer, Torbjörn; Leung, Yee Hong

    2016-12-01

    Objective. In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. Approach. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Main results. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. Significance. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

  8. 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…

  9. A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics

    Directory of Open Access Journals (Sweden)

    Guangkuo Lu

    2015-01-01

    Full Text Available Methods of utilizing independent component analysis (ICA give little guidance about practical considerations for separating single-channel real-world data, in which most of them are nonlinear, nonstationary, and even chaotic in many fields. To solve this problem, a three-step method is provided in this paper. In the first step, the measured signal which is assumed to be piecewise higher order stationary time series is introduced and divided into a series of higher order stationary segments by applying a modified segmentation algorithm. Then the state space is reconstructed and the single-channel signal is transformed into a pseudo multiple input multiple output (MIMO mode using a method of nonlinear analysis based on the high order statistics (HOS. In the last step, ICA is performed on the pseudo MIMO data to decompose the single channel recording into its underlying independent components (ICs and the interested ICs are then extracted. Finally, the effectiveness and excellence of the higher order single-channel ICA (SCICA method are validated with measured data throughout experiments. Also, the proposed method in this paper is proved to be more robust under different SNR and/or embedding dimension via explicit formulae and simulations.

  10. Motion artifact removal algorithm by ICA for e-bra: a women ECG measurement system

    Science.gov (United States)

    Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.

    2013-04-01

    Wearable ECG(ElectroCardioGram) measurement systems have increasingly been developing for people who suffer from CVD(CardioVascular Disease) and have very active lifestyles. Especially, in the case of female CVD patients, several abnormal CVD symptoms are accompanied with CVDs. Therefore, monitoring women's ECG signal is a significant diagnostic method to prevent from sudden heart attack. The E-bra ECG measurement system from our previous work provides more convenient option for women than Holter monitor system. The e-bra system was developed with a motion artifact removal algorithm by using an adaptive filter with LMS(least mean square) and a wandering noise baseline detection algorithm. In this paper, ICA(independent component analysis) algorithms are suggested to remove motion artifact factor for the e-bra system. Firstly, the ICA algorithms are developed with two kinds of statistical theories: Kurtosis, Endropy and evaluated by performing simulations with a ECG signal created by sgolayfilt function of MATLAB, a noise signal including 0.4Hz, 1.1Hz and 1.9Hz, and a weighed vector W estimated by kurtosis or entropy. A correlation value is shown as the degree of similarity between the created ECG signal and the estimated new ECG signal. In the real time E-Bra system, two pseudo signals are extracted by multiplying with a random weighted vector W, the measured ECG signal from E-bra system, and the noise component signal by noise extraction algorithm from our previous work. The suggested ICA algorithm basing on kurtosis or entropy is used to estimate the new ECG signal Y without noise component.

  11. Permutation Tests in Principal Component Analysis.

    Science.gov (United States)

    Pohlmann, John T.; Perkins, Kyle; Brutten, Shelia

    Structural changes in an English as a Second Language (ESL) 30-item reading comprehension test were examined through principal components analysis on a small sample (n=31) of students. Tests were administered on three occasions during intensive ESL training. Principal components analysis of the items was performed for each test occasion.…

  12. EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis.

    Science.gov (United States)

    Valdés-Sosa, Pedro A; Vega-Hernández, Mayrim; Sánchez-Bornot, José Miguel; Martínez-Montes, Eduardo; Bobes, María Antonieta

    2009-06-01

    This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.

  13. Reflection removal in smart devices using a prior assisted independent components analysis

    Science.gov (United States)

    Kalwad, Pramati; Prakash, Divya; Peddigari, Venkat; Srinivasa, Phanish

    2015-02-01

    When photographs are taken through a glass or any other semi-reflecting transparent surface, in museums, shops, aquariums etc., we encounter undesired reflection. Reflection Removal is an ill-posed problem and is caused by superposition of two layers namely the scene in front of camera and the scene behind the camera getting reflected because of the semi-reflective surface. Modern day hand held Smart Devices (smartphones, tablets, phablets, etc) are typically used for capturing scenes as they are equipped with good camera sensors and processing capabilities and we can expect image quality to be similar to a professional camera. In this direction, we propose a novel method to reduce reflection in images, which is an extension of Independent Component Analysis (ICA) approach, by making use of two cameras present - a back camera (capturing actual scene) and a front facing camera. When compared to the original ICA implementation, our method gives on an average of 10% improvement on the peak signal to noise ratio of the image.

  14. 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

  15. EVALUATION OF SOUND CLASSIFICATION USING MODIFIED CLASSIFIER AND SPEECH ENHANCEMENT USING ICA ALGORITHM FOR HEARING AID APPLICATION

    Directory of Open Access Journals (Sweden)

    N. Shanmugapriya

    2016-03-01

    Full Text Available Hearing aid users are exposed to diversified vocal scenarios. The necessity for sound classification algorithms becomes a vital factor to yield good listening experience. In this work, an approach is proposed to improve the speech quality in the hearing aids based on Independent Component Analysis (ICA algorithm with modified speech signal classification methods. The proposed algorithm has better results on speech intelligibility than other existing algorithm and this result has been proved by the intelligibility experiments. The ICA algorithm and modified Bayesian with Adaptive Neural Fuzzy Interference System (ANFIS is to effectiveness of the strategies of speech quality, thus this classification increases noise resistance of the new speech processing algorithm that proposed in this present work. This proposed work indicates that the new Modified classifier can be feasible in hearing aid applications.

  16. Structured Functional Principal Component Analysis

    Science.gov (United States)

    Shou, Haochang; Zipunnikov, Vadim; Crainiceanu, Ciprian M.; Greven, Sonja

    2015-01-01

    Summary Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep. PMID:25327216

  17. 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...... detector is a GEM-3, which is a monostatic sensor measuring the response of the environment on a multi-frequency constant wave excitation field (300 Hz to 25 kHz), and the GPR detector is a stepped-frequency GPR with a monostatic bow-tie antenna (500MHz to 2.5GHz). For both sensors the in...

  18. 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

  19. 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.

  20. Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach

    CERN Document Server

    Ji, Yongnan; Aickelin, Uwe; Pitiot, Alain

    2010-01-01

    Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. F...

  1. A blind video watermarking scheme based on ICA and shot segmentation

    Institute of Scientific and Technical Information of China (English)

    SUN Jiande; LIU Ju

    2006-01-01

    Video watermark is the main method to protect the copyright of digital video. In this paper, a blind video watermarking scheme based on independent component analysis (ICA) and shot segmentation is presented. In this scheme, the global histogram comparison approach is used to segment the video, and ICA is performed on each obtained segment to get its independent component frames (ICFs). The copyright information is embedded into the principal independent component frames (PICFs) according to the single watermark embedding (SWE) scheme. The content-based shot segmentation for video sequences is used here to improve the robustness to temporal desynchronization. The watermark embedded in PICFs provides better robustness to intra-video collusion attack. And blind detection is achieved by using the SWE scheme. The simulations show the feasibility and validity of this scheme. It can resist most of the common frame-based and video-based attacks. The watermark can be detected blindly. And it is robust to temporal desynchronization and intra-video collusion.

  2. 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.

  3. 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...

  4. A group independent component analysis of covert verb generation in children: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Karunanayaka, Prasanna; Schmithorst, Vincent J; Vannest, Jennifer; Szaflarski, Jerzy P; Plante, Elena; Holland, Scott K

    2010-05-15

    Semantic language skills are an integral part of early childhood language development. The semantic association between verbs and nouns constitutes an important building block for the construction of sentences. In this large-scale functional magnetic resonance imaging (fMRI) study, involving 336 subjects between the ages of 5 and 18 years, we investigated the neural correlates of covert verb generation in children. Using group independent component analysis (ICA), seven task-related components were identified including the mid-superior temporal gyrus, the most posterior aspect of the superior temporal gyrus, the parahippocampal gyrus, the inferior frontal gyrus, the angular gyrus, and medial aspect of the parietal lobule (precuneus/posterior cingulate). A highly left-lateralized component was found including the medial temporal gyrus, the frontal gyrus, the inferior frontal gyrus, and the angular gyrus. The associated independent component (IC) time courses were analyzed to investigate developmental changes in the neural elements supporting covert verb generation. Observed age effects may either reflect specific local neuroplastic changes in the neural substrates supporting language or a more global transformation of neuroplasticity in the developing brain. The results are analyzed and presented in the framework of two theoretical models for neurocognitive brain development. In this context, group ICA of fMRI data from our large sample of children aged 5-18 years provides strong evidence in support of the regionally weighted model for cognitive neurodevelopment of language networks.

  5. Generalized Structured Component Analysis with Latent Interactions

    Science.gov (United States)

    Hwang, Heungsun; Ho, Moon-Ho Ringo; Lee, Jonathan

    2010-01-01

    Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA…

  6. 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...

  7. 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.

  8. A Novel Constrained Topographic Independent Component Analysis for Separation of Epileptic Seizure Signals

    Directory of Open Access Journals (Sweden)

    Min Jing

    2007-01-01

    Full Text Available Blind separation of the electroencephalogram signals (EEGs using topographic independent component analysis (TICA is an effective tool to group the geometrically nearby source signals. The TICA algorithm further improves the results if the desired signal sources have particular properties which can be exploited in the separation process as constraints. Here, the spatial-frequency information of the seizure signals is used to design a constrained TICA for the separation of epileptic seizure signal sources from the multichannel EEGs. The performance is compared with those from the TICA and other conventional ICA algorithms. The superiority of the new constrained TICA has been validated in terms of signal-to-interference ratio and correlation measurement.

  9. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis

    Science.gov (United States)

    Nian, Rui; Liu, Fang; He, Bo

    2013-01-01

    Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA) framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs). PMID:23863855

  10. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Bo He

    2013-07-01

    Full Text Available Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs.

  11. Independent component analysis and decision trees for ECG holter recording de-noising.

    Directory of Open Access Journals (Sweden)

    Jakub Kuzilek

    Full Text Available We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA. This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG, but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact were compared.

  12. Extraversion and fronto-posterior EEG spectral power gradient: an independent component analysis.

    Science.gov (United States)

    Knyazev, Gennady G; Bocharov, Andrey V; Pylkova, Liudmila V

    2012-02-01

    Several studies show that the fronto-posterior EEG spectral power gradient is a stable individual characteristic related to personality. Whether this characteristic is specifically related to agentic extraversion and theta band of frequencies or is associated with a broader set of personality traits and frequency bands is a matter of debate, as well as the specific cortical regions contributing to this effect. To clarify these questions, we used group independent component analysis (ICA) and source localization techniques. Agentic extraversion was associated with higher theta activity in the default mode network's (DMN) posterior hub and lower theta activity in the orbitofrontal cortex (OFC). Regression analyses showed that theta activity predicted agentic extraversion better than other frequency bands and agentic extraversion predicted posterior versus frontal activity better than other personality dimensions. These results are taken to indicate higher tonic activity in OFC and lower activity in DMN in extraverts as compared to introverts.

  13. Sparse Principal Component Analysis with missing observations

    CERN Document Server

    Lounici, Karim

    2012-01-01

    In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the high-dimensional setting with missing observations. Our goal is to estimate the first principal component when we only have access to partial observations. Existing estimation techniques are usually derived for fully observed data sets and require a prior knowledge of the sparsity of the first principal component in order to achieve good statistical guarantees. Our contributions is threefold. First, we establish the first information-theoretic lower bound for the sparse PCA problem with missing observations. Second, we propose a simple procedure that does not require any prior knowledge on the sparsity of the unknown first principal component or any imputation of the missing observations, adapts to the unknown sparsity of the first principal component and achieves the optimal rate of estimation up to a logarithmic factor. Third, if the covariance matrix of interest admits a sparse first principal component and is in additi...

  14. ICA if fMRI based on a convolutive mixture model

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2003-01-01

    mixing relevant for spatial ICA. Convolutive ICA has many computational problems and no standard solution is available. In this study a new predictive estimation method is used for finding the mixing coefficients and the source signals of a convolutive mixture and it is applied in temporal mode...... challenge with previous independent component analyses is the convolutive nature of the mixing process in fMRI. In temporal ICA we assume that the measured fMRI response is an instantaneous, spatially varying, mixture of independent time functions. However, the convolutive structure of the hemodynamic....... The mixing is represented by “mixture coefficient images” quantifying the local response to a given source at a certain time lag. This is the first communication to address this important issue in the context of fMRI ICA. Data: A single slice holding 128x128 pixels and passing through primary visual cortex...

  15. Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images.

    Science.gov (United States)

    Kopriva, Ivica; Persin, Antun; Puizina-Ivić, Neira; Mirić, Lina

    2010-07-02

    This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude.

  16. Joint ICA of ERP and fMRI during error-monitoring.

    Science.gov (United States)

    Edwards, Bethany G; Calhoun, Vince D; Kiehl, Kent A

    2012-01-16

    The anterior cingulate cortex (ACC) is commonly separated into two functional divisions: the cognitive division, which lies in the caudal region and the affective division, which lies in the rostral region of the ACC. Both regions of the ACC are engaged during error-monitoring tasks; however, little is known about the temporal sequencing associated with cognition and affective processes during error-monitoring. Here we use joint Independent Component Analysis (jICA) to couple event-related potential (ERP) time courses and functional magnetic resonance imaging (fMRI) spatial maps to examine the spatio-temporal stages of engagement in the two divisions of the ACC during error-monitoring. Consistent with hypotheses, two of the five significant spatio-temporal components identified by jICA revealed that the error-related negativity (ERN) ERP was associated with distinct spatial fMRI patterns in the ACC. The ERN(1) was associated with activity in the caudal ACC and lateral prefrontal cortex (lPFC) while the ERN(2) was associated with activity in the rostral ACC. These results suggest that during error-monitoring the caudal ACC and lPFC engage prior to the rostral ACC. These results suggest that cognition precedes affect during error-monitoring. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

    Science.gov (United States)

    Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

    2017-08-01

    Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

  18. ICA-Based Imagined Conceptual Words Classification on EEG Signals.

    Science.gov (United States)

    Imani, Ehsan; Pourmohammad, Ali; Bagheri, Mahsa; Mobasheri, Vida

    2017-01-01

    Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode. In the first part of this research, the statistical community test case included four men and four women, who were 25-30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator

  19. Principal component analysis of symmetric fuzzy data

    NARCIS (Netherlands)

    Giordani, Paolo; Kiers, Henk A.L.

    2004-01-01

    Principal Component Analysis (PCA) is a well-known tool often used for the exploratory analysis of a numerical data set. Here an extension of classical PCA is proposed, which deals with fuzzy data (in short PCAF), where the elementary datum cannot be recognized exactly by a specific number but by a

  20. Principal Component Analysis in ECG Signal Processing

    Directory of Open Access Journals (Sweden)

    Andreas Bollmann

    2007-01-01

    Full Text Available This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained. Aspects on PCA related to data with temporal and spatial correlations are considered as adaptive estimation of principal components is. Several ECG applications are reviewed where PCA techniques have been successfully employed, including data compression, ST-T segment analysis for the detection of myocardial ischemia and abnormalities in ventricular repolarization, extraction of atrial fibrillatory waves for detailed characterization of atrial fibrillation, and analysis of body surface potential maps.

  1. Beyond noise: using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest

    Directory of Open Access Journals (Sweden)

    Roland Norbert Boubela

    2013-05-01

    Full Text Available Analysis of resting-state networks using fMRI usually ignores high-frequencyfluctuations in the BOLD signal – be it because of low TR prohibiting the analysis offluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s, or becauseof the application of a bandpass filter (commonly restricting the signal to frequencieslower than 0.1 Hz. While the standard model of convolving neuronal activity with ahemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 minutes of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heartbeat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.

  2. 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

  3. 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.

  4. 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.

  5. 基于ICA的遥感图像的色彩分类方法%Classification of Remote Sensing Image Based on Independent Components Analysis

    Institute of Scientific and Technical Information of China (English)

    赵蔷; 刘淑英; 李红

    2013-01-01

    根据独立成分分析( ICA)方法和多频谱卫星遥感图像的特点,提出了一种基于ICA的遥感图像色彩分类法。方法使用Fast ICA算法提取遥感图像的色彩独立成分,是RGB反转的结合,具有互补的分布,不受照明的影响。使用最大相似度分类算法对像素进行色彩分类,实验结果表明,方法的色彩分类效果较好,对多频谱遥感图像进行色彩分类十分有效。%This article propose a classification algorithm for satellite remote sensing images based on Inde-pendent Components Analysis ( ICA) .The algorithm combines the advantage of ICA and multispectral re-motely sensed images .The algorithm extracts the spectral independent components of multispectral re-motely sensed images by Fast ICA algorithm .It is the combine of reversion about R ,G and B,has comple-mentary distribution and is unacted on illumination .Maximum Likelihood is used to classify the pixels . Experimental results demonstrate that the algorithm is an effective improve method to classify the multi-spectral remotely sensed images .

  6. Lung sounds auscultation technology based on ANC-ICA algorithm in high battlefield noise environment

    Institute of Scientific and Technical Information of China (English)

    牛海军; 冯安吉; 万明习; 白培瑞

    2003-01-01

    AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.

  7. 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

  8. 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.

  9. A temporally constrained ICA (TCICA) technique for artery-vein separation of cerebral microvasculature

    Science.gov (United States)

    Mehrabian, Hatef; Lindvere, Liis; Stefanovic, Bojana; Martel, Anne L.

    2010-03-01

    A fully automatic ICA based data driven technique which incorporates additional a priori information from physiological modeling of the cerebral microcirculation (gamma variate model) is developed for the separation of arteries and veins in contrast-enhanced studies of the cerebral microvasculature. A dynamic data set of 50 images taken by a two-photon laser scanning microscopy technique that monitors the passage of a bolus of dye through artery and vein is used here. A temporally constrained ICA (TCICA) technique is developed to extract the vessel specific dynamics of artery and vein by adding two constraints to classical ICA algorithm. One of the constraints guarantees that the extracted curves follow the gamma variate model of blood passage through vessels. Positivity as the second constraint indicates that none of the extracted component images that correspond to the artery, vein or other capillaries in the imaging field of view, has negative impact on the acquired images. Experimental results show improved performance of the proposed temporally constrained ICA (TCICA) over the most commonly used classical ICA technique (fast-ICA) in generating physiologically meaningful curves; they are also closer to that of pixel by pixel model fitting algorithms and perform better in handling noise. This technique is also fully automatic and does not require specifying regions of interest which is critical in model based techniques.

  10. Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

    Science.gov (United States)

    Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

    2008-10-01

    We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions

  11. Stochastic convex sparse principal component analysis.

    Science.gov (United States)

    Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu

    2016-12-01

    Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.

  12. Principal component analysis implementation in Java

    Science.gov (United States)

    Wójtowicz, Sebastian; Belka, Radosław; Sławiński, Tomasz; Parian, Mahnaz

    2015-09-01

    In this paper we show how PCA (Principal Component Analysis) method can be implemented using Java programming language. We consider using PCA algorithm especially in analysed data obtained from Raman spectroscopy measurements, but other applications of developed software should also be possible. Our goal is to create a general purpose PCA application, ready to run on every platform which is supported by Java.

  13. Independent multiresolution component analysis and matching pursuit

    NARCIS (Netherlands)

    E. Capobianco (Enrico)

    2001-01-01

    textabstractWe show that decomposing a class of signals with overcomplete dictionaries of functions and combining multiresolution and independent component analysis allow for feature detection in complex non-stationary high frequency time series. Computational learning techniques are then designed

  14. Principal component analysis of phenolic acid spectra

    Science.gov (United States)

    Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to...

  15. Advanced Placement: Model Policy Components. Policy Analysis

    Science.gov (United States)

    Zinth, Jennifer

    2016-01-01

    Advanced Placement (AP), launched in 1955 by the College Board as a program to offer gifted high school students the opportunity to complete entry-level college coursework, has since expanded to encourage a broader array of students to tackle challenging content. This Education Commission of the State's Policy Analysis identifies key components of…

  16. Self-sustained vibrations in volcanic areas extracted by Independent Component Analysis: a review and new results

    Science.gov (United States)

    de Lauro, E.; de Martino, S.; Falanga, M.; Palo, M.

    2011-12-01

    We investigate the physical processes associated with volcanic tremor and explosions. A volcano is a complex system where a fluid source interacts with the solid edifice so generating seismic waves in a regime of low turbulence. Although the complex behavior escapes a simple universal description, the phases of activity generate stable (self-sustained) oscillations that can be described as a non-linear dynamical system of low dimensionality. So, the system requires to be investigated with non-linear methods able to individuate, decompose, and extract the main characteristics of the phenomenon. Independent Component Analysis (ICA), an entropy-based technique is a good candidate for this purpose. Here, we review the results of ICA applied to seismic signals acquired in some volcanic areas. We emphasize analogies and differences among the self-oscillations individuated in three cases: Stromboli (Italy), Erebus (Antarctica) and Volcán de Colima (Mexico). The waveforms of the extracted independent components are specific for each volcano, whereas the similarity can be ascribed to a very general common source mechanism involving the interaction between gas/magma flow and solid structures (the volcanic edifice). Indeed, chocking phenomena or inhomogeneities in the volcanic cavity can play the same role in generating self-oscillations as the languid and the reed do in musical instruments. The understanding of these background oscillations is relevant not only for explaining the volcanic source process and to make a forecast into the future, but sheds light on the physics of complex systems developing low turbulence.

  17. Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

    Science.gov (United States)

    Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

    2016-11-14

    The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

  18. Boosting Principal Component Analysis by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Divya Somvanshi

    2010-07-01

    Full Text Available This paper presents a new method of feature extraction by combining principal component analysis and genetic algorithm. Use of multiple pre-processors in combination with principal component analysis generates alternate feature spaces for data representation. The present method works out the fusion of these multiple spaces to create higher dimensionality feature vectors. The fused feature vectors are given chromosome representation by taking feature components to be genes. Then these feature vectors are allowed to undergo genetic evolution individually. For genetic algorithm, initial population is created by calculating probability distance matrix, and by applying a probability distance metric such that all the genes which lie farther than a defined threshold are tripped to zero. The genetic evolution of fused feature vector brings out most significant feature components (genes as survivours. A measure of significance is adapted on the basis of frequency of occurrence of the surviving genes in the current population. Finally, the feature vector is obtained by weighting the original feature components in proportion to their significance. The present algorithm is validated in combination with a neural network classifier based on error backpropagation algorithm, and by analysing a number of benchmark datasets available in the open sources.Defence Science Journal, 2010, 60(4, pp.392-398, DOI:http://dx.doi.org/10.14429/dsj.60.495

  19. Multi-component machine monitoring and fault diagnosis using blind source separation and advanced vibration analysis

    Science.gov (United States)

    Mahvash Mohammadi, Ali

    In this dissertation, two approaches are studied for the case of bearing anomaly detection. One approach is to regard it as a blind source separation (cocktail party) problem and take advantage of statistical and mathematical methods developed for this purpose, primarily independent component analysis (ICA), to separate signals coming from different sources. The other approach is to avoid making the effort to 'separate' the signals and relate them to different components (sources) and instead make use of the specification and characteristics of vibration signals produced by the different components in normal and faulty conditions. In the first approach, a common difficulty with applying blind source separation techniques (or, in general any mathematical methods) to separation of vibration sources is that no standard measure exists to assess the quality of separation and validate the results. In fact, for an ideal assessment the true original signals produced by each component must be available as a prerequisite. This requires gathering signals from each component in strict isolation during operation in a lab environment which, if not impossible, is very costly and difficult. To alleviate this difficulty, a novel method is developed that presents the distribution of vibration energy with regard to the respective locations of vibration sources and sensors, and takes into consideration the mechanical attributes of the structure. This method uses some key concepts from statistical energy analysis (SEA) to support the fact that each sensor collects a different version of the oscillations produced in the system with respect to its location in the system. Therefore, by comparing the spectral signature of the vibration signals and making use of a priori knowledge of the spatial distribution of sensors and components, a schematic representation of the spectral signature of the vibration sources are obtained. This method is verified using a series of experiments with

  20. 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.

  1. Focal artifact removal from ongoing EEG--a hybrid approach based on spatially-constrained ICA and wavelet de-noising.

    Science.gov (United States)

    Akhtar, Muhammad Tahir; James, Christopher J

    2009-01-01

    Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially-constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any brain activity from extracted artifacts, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as all ICs are not identified in the usual manner due to the square mixing assumption. Simulation results demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.

  2. fMRI盲信号分离中的时间和空间独立成分分析法的时空特性比较%Comparison of Spatiotemporal Characteristics of Spatial and Temporal Independent Component Analysis for Blind Source Separation in fMRI Data

    Institute of Scientific and Technical Information of China (English)

    高欣; 边倩; 熊金虎

    2012-01-01

    目的 采用空间独立成分分析法( siCA)和时间独立成分分析法(tICA)对功能磁共振成像(fMRI)信号进行分离,比较信号间的时空特性对2种独立成分分析方法性能的影响.方法 模拟fMRI数据,并将2组独立的信号以及它们的线性混合信号叠加到空间独立的区域,分别利用Infomax、Combi、FBSS和ICA-EMB 4种算法实现sICA和tICA,并对模拟数据中的3组信号进行提取和分离.结果 sICA只能分离空间独立且时间高度独立的信号,无法分离空间相关、时间独立的信号;tICA不仅能够准确分离空间和时间高度独立信号,而且能够准确分离空间高度相关、时间独立的信号,并将时间相关信号整体提取;FBSS和ICA-EMB 2种算法较Infomax和Combi性能稳定.结论 空间或时间独立性假设违背到一定程度时,slCA和tICA对信号分离的结果存在差异.应根据需要选择适合的sICA或者tICA方法对fMRI数据进行处理.%Objective Separate the fMRI signals by using spatial independent component analysis (sICA) and temporal independent component analysis (tICA). Compare the spatiotemporal characteristics among signals which has effect to performance analysis of two kinds of independt signals. Method Simulate fMRI data. Two sets of independent signals and their linear mixture were added in spatially independent regions. sICA and tICA were achieved by using respectively Infomax, Combi, FBSS and ICA-LMB. Then three sets of signals were applied to separate and extract from simulated fMRI. Results sJCA can only separate signals spatially independent and highly temporally independent, can not separate spatially correlated and temporally independent signals. tlCAis not only able to separate highly spatially and temporally independent signals, but also able to successfully separate highly spatially correlated and temporally independent signals, moreover, it can integrally extract temporally correlated signals. FBSS and ICA

  3. Image classification based on ICA-WP feature of EEG signal.

    Science.gov (United States)

    Zhu, Wei; Zhang, Han; Ni, Weiping; Xu, Xiong; Wu, Junzheng

    2016-04-29

    In this paper, a method for classifying electroencephalographic (EEG) recordings with images as stimulation is introduced, which aims at selecting the target images. EEG recordings to be processed are referred to the onset of the test images with a single stimulation so as to avoid spending extra time on repeating images. Independent component analysis (ICA) is used to reduce the redundancy of EEG recordings, and wavelet packet (WP) analysis is efficient for dealing with the non-stationary character of brain activity. Feature vectors are extracted by a method that combines these two algorithms. The support vector machine is used as a classifier, carrying out the classification result. The experimental results demonstrate that the accuracy of this method's image classification is affected very little by different classifier parameters. The best result achieves 90% accuracy, which indicts it is feasible for classifying images with a single stimulation.

  4. Altered brain activation and functional connectivity in working memory related networks in patients with type 2 diabetes: An ICA-based analysis.

    Science.gov (United States)

    Zhang, Yang; Lu, Shan; Liu, Chunlei; Zhang, Huimei; Zhou, Xuanhe; Ni, Changlin; Qin, Wen; Zhang, Quan

    2016-03-29

    Type 2 diabetes mellitus (T2DM) can cause multidimensional cognitive deficits, among which working memory (WM) is usually involved at an early stage. However, the neural substrates underlying impaired WM in T2DM patients are still unclear. To clarify this issue, we utilized functional magnetic resonance imaging (fMRI) and independent component analysis to evaluate T2DM patients for alterations in brain activation and functional connectivity (FC) in WM networks and to determine their associations with cognitive and clinical variables. Twenty complication-free T2DM patients and 19 matched healthy controls (HCs) were enrolled, and fMRI data were acquired during a block-designed 1-back WM task. The WM metrics of the T2DM patients showed no differences compared with those of the HCs, except for a slightly lower accuracy rate in the T2DM patients. Compared with the HCs, the T2DM patients demonstrated increased activation within their WM fronto-parietal networks, and activation strength was significantly correlated with WM performance. The T2DM patients also showed decreased FC within and between their WM networks. Our results indicate that the functional integration of WM sub-networks was disrupted in the complication-free T2DM patients and that strengthened regional activity in fronto-parietal networks may compensate for the WM impairment caused by T2DM.

  5. Translation of EEG spatial filters from resting to motor imagery using independent component analysis.

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    Full Text Available Electroencephalogram (EEG-based brain-computer interfaces (BCIs often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%, which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%. The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters.

  6. 临床分离表皮葡萄球菌的耐药性分析及与icaD基因表达关系的研究%Analysis of drug resistance and relationship with icaD genetypes of clinically isolated staphylococcus epidermidis

    Institute of Scientific and Technical Information of China (English)

    曹晓光; 周树生; 戴媛媛; 刘宝

    2012-01-01

    Objective To detect the drug resistance of our ICU staphylococcus epidermidis to antibiotics, and to provide evidence for reasonable prevention and control measures to guide clinical medication . Methods mecA gene and icaD gene of the 125 strains staphylococcus epidermidis from clinical specimens and healthy skin were inspected and analyzed the drug resistance by K -B disk diffusion method and molecular biology method Results In two groups, icaD gene expression was correlated with mecA' s ( r = 0. 528, P = 0. 000; r = 0. 309, P = 0. 016 ) MecA gene expression in humor of clinical patients group was significantly higher than that in non -humor of normal people, and there was significant difference (P <0. 01) , while there was no significant difference in bacterial strain of icaD gene expression. Staphylococcus epidermidis in two groups were sensitive to rifampicin , nitrofurantoin, linezol-id and Vancomycin. The resistant rates of mecA gene expression bacterial strain to antibacterial agents had no sig -nificant difference in two groups , but the resistant rates of icaD gene expression had significant difference in ampi -cillin, cefoxitin, bactrim, erythromycin and chloramphenicol( P < 0. 05 ) Conclusion That ICU staphylococcus epidermidis to antibacterial agents have high drug resistance ,we must strengthen resistance monitoring, and according to antimicrobial susceptibility test results to choose rational drug , control nosocomial infection.%目的 了解我院ICU表皮葡萄球菌对抗生素的耐药性,为制定合理的预防控制措施,指导临床用药提供依据.方法 采用K-B纸片扩散法和分子生物学方法 对临床标本和健康人皮肤分离的共125株表皮葡萄球菌的耐药性及其mecA和icaD基因进行检测分析.结果 ①两组在icaD基因的表达与mecA基因表达均存在相关性(r=0.528,P=0.000;r=0.309,P=0.016);②mecA基因表达在临床患者体液组中较健康自愿者非体液组明显增

  7. Principal components analysis of Jupiter VIMS spectra

    Science.gov (United States)

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

    2004-01-01

    During Cassini - Jupiter flyby occurred in December 2000, Visual-Infrared mapping spectrometer (VIMS) instrument took several image cubes of Jupiter at different phase angles and distances. We have analysed the spectral images acquired by the VIMS visual channel by means of a principal component analysis technique (PCA). The original data set consists of 96 spectral images in the 0.35-1.05 ??m wavelength range. The product of the analysis are new PC bands, which contain all the spectral variance of the original data. These new components have been used to produce a map of Jupiter made of seven coherent spectral classes. The map confirms previously published work done on the Great Red Spot by using NIMS data. Some other new findings, presently under investigation, are presented. ?? 2004 Published by Elsevier Ltd on behalf of COSPAR.

  8. Wall Clutter Mitigation in Through-the-Wall Imaging Radar with Sparse Array Antenna Based on Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2014-10-01

    Full Text Available For Through-the-Wall Imaging Radar (TWIR, wall clutter is critical for detecting target signals behind a wall. For a system with a sparse antenna array, the lack of observation channels makes it more difficult to separate the target signals and wall clutter. On the basis of fluctuation of the range profile in real transmit/receive channels, this paper proposes to use Independent Component Analysis (ICA on multiple down-range observations of each transmit/receive channel to remove the wall clutter. The simulation and experimental results show that the proposed method effectively separate target and clutter components, even though the signal-to-clutter ratio is only -30 dB.

  9. Principal component analysis for authorship attribution

    Directory of Open Access Journals (Sweden)

    Amir Jamak

    2012-01-01

    Full Text Available Background: To recognize the authors of the texts by the use of statistical tools, one first needs to decide about the features to be used as author characteristics, and then extract these features from texts. The features extracted from texts are mostly the counts of so called function words. Objectives: The data extracted are processed further to compress as a data with less number of features, such a way that the compressed data still has the power of effective discriminators. In this case feature space has less dimensionality then the text itself. Methods/Approach: In this paper, the data collected by counting words and characters in around a thousand paragraphs of each sample book, underwent a principal component analysis performed using neural networks. Once the analysis was complete, the first of the principal components is used to distinguish the books authored by a certain author. Results: The achieved results show that every author leaves a unique signature in written text that can be discovered by analyzing counts of short words per paragraph. Conclusions: In this article we have demonstrated that based on analyzing counts of short words per paragraph authorship could be traced using principal component analysis. Methodology could be used for other purposes, like fraud detection in auditing.

  10. 独立成份分析在径向基函数网中的应用研究%STUDY ON APPLICATION OF INDEPENDENT COMPONENT ANALYSIS TO RADIAL BASIS FUNCTION NETWORK

    Institute of Scientific and Technical Information of China (English)

    郭穗勋; 黄榕波

    2004-01-01

    提出应用独立成份分析(Independent Component Analysis,ICA)降低径向基函数网(RBFN)输入维数的方法,并讨论ICA降维方法对RBFN行为的影响.实验表明基于ICA的降维方法大大提高了RBFN的收敛速度,改善RBFN的行为.

  11. REVIEW: Previous Deception detection methods and New proposed method using independent component analysis of EEG signals.

    Directory of Open Access Journals (Sweden)

    Roshni D. Tale

    2014-04-01

    Full Text Available Deception detection has important legal and medical applications, but the reliability of methods for the differentiation between truthful and deceptive responses is still limited. Deception detection can be more accurately achieved by measuring the brain correlates of lying in an individual. For the evaluation of the method, several participants were gone through the designed concealed information test paradigm and their respective brain signals were recorded. The electroencephalogram (EEG signals were recorded and separated into many single trials. To enhance signal noise ratio (SNR of P3 components, the independent component analysis (ICA method was adopted to separate non-P3 (i.e. artifacts and P3 components from every single trial. Then the P3 waveforms with high SNR were reconstructed. And then group of features based on time, frequency, and amplitude were extracted from the reconstructed P3 waveforms. Finally, two different class of feature samples were used to train a support vector machine (SVM classifier because it has higher performance compared with several other classifiers. The method presented in this paper improves the efficiency of CIT and deception detection in comparison with previous reported methods.

  12. 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.

  13. 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

  14. Accelerated FEM Analysis for Critical Engine Components

    Directory of Open Access Journals (Sweden)

    Leonardo FRIZZIERO

    2014-10-01

    Full Text Available This paper introduces a method to simplify a nonlinear problem in order to use linear finite element analysis. This approach improves calculation time by 2 orders of magnitude. It is then possible to optimize the geometry of the components even without supercomputers. In this paper the method is applied to a very critical component: the aluminium alloy piston of a modern common rail diesel engine. The method consists in the subdivision of the component, in this case the piston, in several volumes, that have approximately a constant temperature. These volumes are then assembled through congruence constraints. To each volume a proper material is then assigned. It is assumed that material behaviour depends on average temperature, load magnitude and load gradient. This assumption is valid since temperatures vary slowly when compared to pressure (load. In fact pressures propagate with the speed of sound. The method is validated by direct comparison with nonlinear simulation of the same component, the piston, taken as an example. In general, experimental tests have confirmed the cost-effectiveness of this approach.

  15. Blind component separation in wavelet space. Application to CMB analysis

    CERN Document Server

    Moudden, Y; Starck, J L; Delabrouille, J

    2004-01-01

    It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse astrophysical emissions in Cosmic Microwave Background observations. We show here that wavelets, which are standard tools in processing non stationary data, can profitably be used to extend SMICA. Among possible applications, it is shown that gaps in data are dealt with more conveniently and with better results using this extension, wSMICA, in place of the original SMICA. The performances of these two methods are compared on simulated CMB data sets, demonstrating the advantageous use of wavelets.

  16. 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.

  17. Real-Time Principal-Component Analysis

    Science.gov (United States)

    Duong, Vu; Duong, Tuan

    2005-01-01

    A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer.

  18. Scaling in ANOVA-simultaneous component analysis.

    Science.gov (United States)

    Timmerman, Marieke E; Hoefsloot, Huub C J; Smilde, Age K; Ceulemans, Eva

    In omics research often high-dimensional data is collected according to an experimental design. Typically, the manipulations involved yield differential effects on subsets of variables. An effective approach to identify those effects is ANOVA-simultaneous component analysis (ASCA), which combines analysis of variance with principal component analysis. So far, pre-treatment in ASCA received hardly any attention, whereas its effects can be huge. In this paper, we describe various strategies for scaling, and identify a rational approach. We present the approaches in matrix algebra terms and illustrate them with an insightful simulated example. We show that scaling directly influences which data aspects are stressed in the analysis, and hence become apparent in the solution. Therefore, the cornerstone for proper scaling is to use a scaling factor that is free from the effect of interest. This implies that proper scaling depends on the effect(s) of interest, and that different types of scaling may be proper for the different effect matrices. We illustrate that different scaling approaches can greatly affect the ASCA interpretation with a real-life example from nutritional research. The principle that scaling factors should be free from the effect of interest generalizes to other statistical methods that involve scaling, as classification methods.

  19. New detector for spread-spectrum based image watermarking using underdetermined ICA

    Science.gov (United States)

    Malik, Hafiz; Khokhar, Ashfaq; Ansari, Rashid

    2006-02-01

    This paper presents a novel scheme for detection of watermarks embedded in multimedia signals using spread spectrum (SS) techniques. The detection method is centered on using the model that the embedded watermark and the host signal are mutually independent. The proposed detector assumes that the host signal and the watermark obey non-Gaussian distributions. The proposed blind watermark detector employs underdetermined blind source separation (BSS) based on independent component analysis (ICA) for watermark estimation from the watermarked image. The mean-field theory based undetermined BSS scheme is used for watermark estimation. Analytical results are presented showing that the proposed detector performs significantly better than the existing correlation based blind detectors traditionally used for SS-based image watermarking.

  20. Continuous motion decoding from EMG using independent component analysis and adaptive model training.

    Science.gov (United States)

    Zhang, Qin; Xiong, Caihua; Chen, Wenbin

    2014-01-01

    Surface Electromyography (EMG) is popularly used to decode human motion intention for robot movement control. Traditional motion decoding method uses pattern recognition to provide binary control command which can only move the robot as predefined limited patterns. In this work, we proposed a motion decoding method which can accurately estimate 3-dimensional (3-D) continuous upper limb motion only from multi-channel EMG signals. In order to prevent the muscle activities from motion artifacts and muscle crosstalk which especially obviously exist in upper limb motion, the independent component analysis (ICA) was applied to extract the independent source EMG signals. The motion data was also transferred from 4-manifold to 2-manifold by the principle component analysis (PCA). A hidden Markov model (HMM) was proposed to decode the motion from the EMG signals after the model trained by an adaptive model identification process. Experimental data were used to train the decoding model and validate the motion decoding performance. By comparing the decoded motion with the measured motion, it is found that the proposed motion decoding strategy was feasible to decode 3-D continuous motion from EMG signals.

  1. 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).

  2. 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

  3. Controlling a Rehabilitation Robot with Brain-Machine Interface: An approach based on Independent Component Analysis and Multiple Kernel Learning

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2013-03-01

    Full Text Available Patients suffering from severe motor disabilities usually require assistance from other people when doing rehabilitation exercises, which causes the rehabilitation process to be time-consuming and inconvenient. Therefore, we propose an automatic feature extraction method for a brain-machine interface that allows patients to control a robot using their own brain waves. A brain–machine interface (BMI based on the P300 event-related potential (ERP, called Brain Controlled Rehabilitation System (BCRS, was developed to detect the intentions of patients. Using the BCRS, patients can communicate with the robot through their brain waves. However, deciding how to obtain an automatically extracted, useful EEG signal is a difficult and important problem for BMI research. In this paper, Independent Component Analysis – Multiple Kernel Learning (ICA-MKL is used to directly extract a useful signal and build the classification mode for BCRS. The results reveal that this method is useful for automatically extracting the P300 signal and the accuracy is better than MKL. In additional, the same method can be extended into any motor imaginary area and the accuracy of ICA-MKL for brain imaginary data is also good to removing eye-blink artifacts and the accuracy performance is also good.

  4. Practical Issues in Component Aging Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Dana L. Kelly; Andrei Rodionov; Jens Uwe-Klugel

    2008-09-01

    This paper examines practical issues in the statistical analysis of component aging data. These issues center on the stochastic process chosen to model component failures. The two stochastic processes examined are repair same as new, leading to a renewal process, and repair same as old, leading to a nonhomogeneous Poisson process. Under the first assumption, times between failures can treated as statistically independent observations from a stationary process. The common distribution of the times between failures is called the renewal distribution. Under the second process, the times between failures will not be independently and identically distributed, and one cannot simply fit a renewal distribution to the cumulative failure times or the times between failures. The paper illustrates how the assumption made regarding the repair process is crucial to the analysis. Besides the choice of stochastic process, other issues that are discussed include qualitative graphical analysis and simple nonparametric hypothesis tests to help judge which process appears more appropriate. Numerical examples are presented to illustrate the issues discussed in the paper.

  5. Independent Component Analysis in Multimedia Modeling

    DEFF Research Database (Denmark)

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

    2003-01-01

    Modeling of multimedia and multimodal data becomes increasingly important with the digitalization of the world. The objective of this paper is to demonstrate the potential of independent component analysis and blind sources separation methods for modeling and understanding of multimedia data, which...... largely refers to text, images/video, audio and combinations of such data. We review a number of applications within single and combined media with the hope that this might provide inspiration for further research in this area. Finally, we provide a detailed presentation of our own recent work on modeling...... combined text/image data for the purpose of cross-media retrieval....

  6. Independent Component Analysis in Multimedia Modeling

    DEFF Research Database (Denmark)

    Larsen, Jan

    Modeling of multimedia and multimodal data becomes increasingly important with the digitalization of the world. The objective of this paper is to demonstrate the potential of independent component analysis and blind sources separation methods for modeling and understanding of multimedia data, which...... largely refers to text, images/video, audio and combinations of such data. We review a number of applications within single and combined media with the hope that this might provide inspiration for further research in this area. Finally, we provide a detailed presentation of our own recent work on modeling...... combined text/image data for the purpose of cross-media retrieval....

  7. Radar fall detection using principal component analysis

    Science.gov (United States)

    Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

    2016-05-01

    Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

  8. Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia.

    Science.gov (United States)

    Sui, Jing; He, Hao; Liu, Jingyu; Yu, Qingbao; Adali, Tulay; Pearlson, Godfrey D; Calhoun, Vince D

    2012-01-01

    Multi-modal fusion is an effective approach in biomedical imaging which combines multiple data types in a joint analysis and overcomes the problem that each modality provides a limited view of the brain. In this paper, we propose an exploratory fusion model, we term "mCCA+jICA", by combining two multivariate approaches: multi-set canonical correlation analysis (mCCA) and joint independent component analysis (jICA). This model can freely combine multiple, disparate data sets and explore their joint information in an accurate and effective manner, so that high decomposition accuracy and valid modal links can be achieved simultaneously. We compared mCCA+jICA with its alternatives in simulation and applied it to real fMRI-DTI-methylation data fusion, to identify brain abnormalities in schizophrenia. The results replicate previous reports and add to our understanding of the neural correlates of schizophrenia, and suggest more generally a promising approach to identify potential brain illness biomarkers.

  9. 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... Analysis Framework 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA2386-15-1-4068 5c.  PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S) Keiji Takeda 5d

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Zhiqiang Guo

    Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA 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, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA 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.

  12. Nonlinear principal component analysis and its applications

    CERN Document Server

    Mori, Yuichi; Makino, Naomichi

    2016-01-01

    This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...

  13. Unbiased Group-Level Statistical Assessment of Independent Component Maps by Means of Automated Retrospective Matching

    NARCIS (Netherlands)

    Langers, Dave R. M.

    2010-01-01

    This report presents and validates a method for the group-level statistical assessment of independent component analysis (ICA) outcomes. The method is based on a matching of individual component maps to corresponding aggregate maps that are obtained from concatenated data. Group-level statistics are

  14. Emotional responses as independent components in EEG

    DEFF Research Database (Denmark)

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

    2014-01-01

    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...... 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...... scalp maps and time series responses we identify neural signatures that are differentially modulated when passively viewing neutral, pleasant and unpleasant images. While early responses can be detected from the raw EEG signal we identify multiple early and late ICA components that are modulated...

  15. 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.

  16. Local Component Analysis for Nonparametric Bayes Classifier

    CERN Document Server

    Khademi, Mahmoud; safayani, Meharn

    2010-01-01

    The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...

  17. Face Recognition Based on Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Ali Javed

    2013-02-01

    Full Text Available The purpose of the proposed research work is to develop a computer system that can recognize a person by comparing the characteristics of face to those of known individuals. The main focus is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background will be constant. All the other methods of person’s identification and verification like iris scan or finger print scan require high quality and costly equipment’s but in face recognition we only require a normal camera giving us a 2-D frontal image of the person that will be used for the process of the person’s recognition. Principal Component Analysis technique has been used in the proposed system of face recognition. The purpose is to compare the results of the technique under the different conditions and to find the most efficient approach for developing a facial recognition system

  18. Principal Components Analysis In Medical Imaging

    Science.gov (United States)

    Weaver, J. B.; Huddleston, A. L.

    1986-06-01

    Principal components analysis, PCA, is basically a data reduction technique. PCA has been used in several problems in diagnostic radiology: processing radioisotope brain scans (Ref.1), automatic alignment of radionuclide images (Ref. 2), processing MRI images (Ref. 3,4), analyzing first-pass cardiac studies (Ref. 5) correcting for attenuation in bone mineral measurements (Ref. 6) and in dual energy x-ray imaging (Ref. 6,7). This paper will progress as follows; a brief introduction to the mathematics of PCA will be followed by two brief examples of how PCA has been used in the literature. Finally my own experience with PCA in dual-energy x-ray imaging will be given.

  19. Integrating Data Transformation in Principal Components Analysis

    KAUST Repository

    Maadooliat, Mehdi

    2015-01-02

    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 to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.

  20. 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.

  1. Principal components analysis of population admixture.

    Directory of Open Access Journals (Sweden)

    Jianzhong Ma

    Full Text Available With the availability of high-density genotype information, principal components analysis (PCA is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences about population relationships from the results of PCA, especially when admixed individuals are included in the analysis. We extend our recently developed theoretical formulation of PCA to allow for admixed populations. Because the sampled individuals are treated as features, our generalized formulation of PCA directly relates the pattern of the scatter plot of the top eigenvectors to the admixture proportions and parameters reflecting the population relationships, and thus can provide valuable guidance on how to properly interpret the results of PCA in practice. Using our formulation, we theoretically justify the diagnostic of two-way admixture. More importantly, our theoretical investigations based on the proposed formulation yield a diagnostic of multi-way admixture. For instance, we found that admixed individuals with three parental populations are distributed inside the triangle formed by their parental populations and divide the triangle into three smaller triangles whose areas have the same proportions in the big triangle as the corresponding admixture proportions. We tested and illustrated these findings using simulated data and data from HapMap III and the Human Genome Diversity Project.

  2. Investigation of human visual cortex responses to flickering light using functional near infrared spectroscopy and constrained ICA

    Directory of Open Access Journals (Sweden)

    Nguyen Duc Thang

    2014-11-01

    Full Text Available The human visual sensitivity to the flickering light has been under investigation for decades. The finding of research in this area can contribute to the understanding of human visual system mechanism and visual disorders, and establishing diagnosis and treatment of diseases. The aim of this study is to investigate the effects of the flickering light to the visual cortex by monitoring the hemodynamic responses of the brain with the functional near infrared spectroscopy (fNIRS method. Since the acquired fNIRS signals are affected by physiological factors and measurement artifacts, constrained independent component analysis (cICA was applied to extract the actual fNIRS responses from the obtained data. The experimental results revealed significant changes (p < 0.0001 of the hemodynamic responses of the visual cortex from the baseline when the flickering stimulation was activated. With the uses of cICA, the contrast to noise ratio (CNR, reflecting the contrast of hemodynamic concentration between rest and task, became larger. This indicated the improvement of the fNIRS signals when the noise was eliminated. In subsequent studies, statistical analysis was used to infer the correlation between the fNIRS signals and the visual stimulus. We found that there was a slight decrease of the oxygenated hemoglobin concentration (about 5.69% over four frequencies when the modulation increased. However, the variations of oxy and deoxy-hemoglobin were not statistically significant.

  3. Gene set analysis using variance component tests

    Science.gov (United States)

    2013-01-01

    Background Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. Results We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). Conclusion We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data. PMID:23806107

  4. Revisiting Spitzer transit observations with Independent Component Analysis: new results for the GJ436 system

    CERN Document Server

    Morello, G; Tinetti, G; Howarth, I D; Micela, G; Allard, F

    2015-01-01

    We analyzed four Spitzer/IRAC observations at 3.6 and 4.5 {\\mu}m of the primary transit of the exoplanet GJ436b, by using blind source separation techniques. These observations are important to investigate the atmospheric composition of the planet GJ436b. Previous analyses claimed strong inter-epoch variations of the transit parameters due to stellar variability, casting doubts on the possibility to extract conclusively an atmospheric signal; those analyses also reported discrepant results, hence the necessity of this reanalysis. The method we used has been proposed in Morello et al. (2014) to analyze 3.6 {\\mu}m transit light-curves of the hot Jupiter HD189733b; it performes an Independent Component Analysis (ICA) on a set of pixel-light-curves, i.e. time series read by individual pixels, from the same photometric observation. Our method only assumes the independence of instrumental and astrophysical signals, and therefore guarantees a higher degree of objectivity compared to parametric detrending techniques ...

  5. Self-sustained vibrations in volcanic areas extracted by Independent Component Analysis: a review and new results

    Directory of Open Access Journals (Sweden)

    E. De Lauro

    2011-12-01

    Full Text Available We investigate the physical processes associated with volcanic tremor and explosions. A volcano is a complex system where a fluid source interacts with the solid edifice so generating seismic waves in a regime of low turbulence. Although the complex behavior escapes a simple universal description, the phases of activity generate stable (self-sustained oscillations that can be described as a non-linear dynamical system of low dimensionality. So, the system requires to be investigated with non-linear methods able to individuate, decompose, and extract the main characteristics of the phenomenon. Independent Component Analysis (ICA, an entropy-based technique is a good candidate for this purpose. Here, we review the results of ICA applied to seismic signals acquired in some volcanic areas. We emphasize analogies and differences among the self-oscillations individuated in three cases: Stromboli (Italy, Erebus (Antarctica and Volcán de Colima (Mexico. The waveforms of the extracted independent components are specific for each volcano, whereas the similarity can be ascribed to a very general common source mechanism involving the interaction between gas/magma flow and solid structures (the volcanic edifice. Indeed, chocking phenomena or inhomogeneities in the volcanic cavity can play the same role in generating self-oscillations as the languid and the reed do in musical instruments. The understanding of these background oscillations is relevant not only for explaining the volcanic source process and to make a forecast into the future, but sheds light on the physics of complex systems developing low turbulence.

  6. ErpICASSO: a tool for reliability estimates of independent components in EEG event-related analysis.

    Science.gov (United States)

    Artoni, Fiorenzo; Gemignani, Angelo; Sebastiani, Laura; Bedini, Remo; Landi, Alberto; Menicucci, Danilo

    2012-01-01

    Independent component analysis and blind source separation methods are steadily gaining popularity for separating individual brain and non-brain source signals mixed by volume conduction in electroencephalographic data. Despite the advancements on these techniques, determining the number of embedded sources and their reliability are still open issues. In particular to date no method takes into account trial-to-trial variability in order to provide a reliability measure of independent components extracted in Event Related Potentials (ERPs) studies. In this work we present ErpICASSO, a new method which modifies a data-driven approach named ICASSO for the analysis of trials (epochs). In addition to ICASSO the method enables the user to estimate the number of embedded sources, and provides a quality index of each extracted ERP component by combining trial-to-trial bootstrapping and CCA projection. We applied ErpICASSO on ERPs recorded from 14 subjects presented with unpleasant and neutral pictures. We separated potentials putatively related to different systems and identified the four primary ERP independent sources. Standing on the confidence interval estimated by ErpICASSO, we were able to compare the components between neutral and unpleasant conditions. ErpICASSO yielded encouraging results, thus providing the scientific community with a useful tool for ICA signal processing whenever dealing with trials recorded in different conditions.

  7. 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.

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

    Science.gov (United States)

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

    2009-10-01

    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.

  9. A radiographic analysis of implant component misfit.

    LENUS (Irish Health Repository)

    Sharkey, Seamus

    2011-07-01

    Radiographs are commonly used to assess the fit of implant components, but there is no clear agreement on the amount of misfit that can be detected by this method. This study investigated the effect of gap size and the relative angle at which a radiograph was taken on the detection of component misfit. Different types of implant connections (internal or external) and radiographic modalities (film or digital) were assessed.

  10. Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data.

    Science.gov (United States)

    Ramkumar, Pavan; Parkkonen, Lauri; Hyvärinen, Aapo

    2014-02-01

    We developed a data-driven method to spatiotemporally and spectrally characterize the dynamics of brain oscillations in resting-state magnetoencephalography (MEG) data. The method, called envelope spatial Fourier independent component analysis (eSFICA), maximizes the spatial and spectral sparseness of Fourier energies of a cortically constrained source current estimate. We compared this method using a simulated data set against 5 other variants of independent component analysis and found that eSFICA performed on par with its temporal variant, eTFICA, and better than other ICA variants, in characterizing dynamics at time scales of the order of minutes. We then applied eSFICA to real MEG data obtained from 9 subjects during rest. The method identified several networks showing within- and cross-frequency inter-areal functional connectivity profiles which resemble previously reported resting-state networks, such as the bilateral sensorimotor network at ~20Hz, the lateral and medial parieto-occipital sources at ~10Hz, a subset of the default-mode network at ~8 and ~15Hz, and lateralized temporal lobe sources at ~8Hz. Finally, we interpreted the estimated networks as spatiospectral filters and applied the filters to obtain the dynamics during a natural stimulus sequence presented to the same 9 subjects. We observed occipital alpha modulation to visual stimuli, bilateral rolandic mu modulation to tactile stimuli and video clips of hands, and the temporal lobe network modulation to speech stimuli, but no modulation of the sources in the default-mode network. We conclude that (1) the proposed method robustly detects inter-areal cross-frequency networks at long time scales, (2) the functional relevance of the resting-state networks can be probed by applying the obtained spatiospectral filters to data from measurements with controlled external stimulation.

  11. Using joint ICA to link function and structure using MEG and DTI in schizophrenia.

    Science.gov (United States)

    Stephen, J M; Coffman, B A; Jung, R E; Bustillo, J R; Aine, C J; Calhoun, V D

    2013-12-01

    In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p<0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170 ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400 ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment

  12. A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

    Institute of Scientific and Technical Information of China (English)

    ZHANG Fei; TASHPOLAT Tiyip; KUNG Hsiang-te; DING Jian-li; MAMAT.Sawut; VERNER Johnson; HAN Gui-hong; GUI Dong-wei

    2011-01-01

    Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM+ Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by 10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

  13. Analysis of complications after blood components' transfusions.

    Science.gov (United States)

    Timler, Dariusz; Klepaczka, Jadwiga; Kasielska-Trojan, Anna; Bogusiak, Katarzyna

    2015-04-01

    Complications after blood components still constitute an important clinical problem and serve as limitation of liberal-transfusion strategy. The aim of the study was to present the 5-year incidence of early blood transfusions complications and to assess their relation to the type of the transfused blood components. 58,505 transfusions of blood components performed in the years 2006-2010 were retrospectively analyzed. Data concerning the amount of the transfused blood components and the numbers of adverse transfusion reactions reported to the Regional Blood Donation and Treatment Center (RBDTC) was collected. 95 adverse transfusion reactions were reportedto RBDTC 0.16% of alldonations (95/58 505) - 58 after PRBC transfusions, 28 after platelet concentrate transfusions and 9 after FFP transfusion. Febrile nonhemolytic and allergic reactions constitute respectively 36.8% and 30.5% of all complications. Nonhemolyticand allergic reactions are the most common complications of blood components transfusion and they are more common after platelet concentrate transfusions in comparison to PRBC and FFP donations.

  14. Spectral Components Analysis of Diffuse Emission Processes

    Energy Technology Data Exchange (ETDEWEB)

    Malyshev, Dmitry; /KIPAC, Menlo Park

    2012-09-14

    We develop a novel method to separate the components of a diffuse emission process based on an association with the energy spectra. Most of the existing methods use some information about the spatial distribution of components, e.g., closeness to an external template, independence of components etc., in order to separate them. In this paper we propose a method where one puts conditions on the spectra only. The advantages of our method are: 1) it is internal: the maps of the components are constructed as combinations of data in different energy bins, 2) the components may be correlated among each other, 3) the method is semi-blind: in many cases, it is sufficient to assume a functional form of the spectra and determine the parameters from a maximization of a likelihood function. As an example, we derive the CMB map and the foreground maps for seven yeas of WMAP data. In an Appendix, we present a generalization of the method, where one can also add a number of external templates.

  15. Mapping ash properties using principal components analysis

    Science.gov (United States)

    Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Ubeda, Xavier; Novara, Agata; Francos, Marcos; Rodrigo-Comino, Jesus; Bogunovic, Igor; Khaledian, Yones

    2017-04-01

    In post-fire environments ash has important benefits for soils, such as protection and source of nutrients, crucial for vegetation recuperation (Jordan et al., 2016; Pereira et al., 2015a; 2016a,b). The thickness and distribution of ash are fundamental aspects for soil protection (Cerdà and Doerr, 2008; Pereira et al., 2015b) and the severity at which was produced is important for the type and amount of elements that is released in soil solution (Bodi et al., 2014). Ash is very mobile material, and it is important were it will be deposited. Until the first rainfalls are is very mobile. After it, bind in the soil surface and is harder to erode. Mapping ash properties in the immediate period after fire is complex, since it is constantly moving (Pereira et al., 2015b). However, is an important task, since according the amount and type of ash produced we can identify the degree of soil protection and the nutrients that will be dissolved. The objective of this work is to apply to map ash properties (CaCO3, pH, and select extractable elements) using a principal component analysis (PCA) in the immediate period after the fire. Four days after the fire we established a grid in a 9x27 m area and took ash samples every 3 meters for a total of 40 sampling points (Pereira et al., 2017). The PCA identified 5 different factors. Factor 1 identified high loadings in electrical conductivity, calcium, and magnesium and negative with aluminum and iron, while Factor 3 had high positive loadings in total phosphorous and silica. Factor 3 showed high positive loadings in sodium and potassium, factor 4 high negative loadings in CaCO3 and pH, and factor 5 high loadings in sodium and potassium. The experimental variograms of the extracted factors showed that the Gaussian model was the most precise to model factor 1, the linear to model factor 2 and the wave hole effect to model factor 3, 4 and 5. The maps produced confirm the patternd observed in the experimental variograms. Factor 1 and 2

  16. Principal component analysis of psoriasis lesions images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A set of RGB images of psoriasis lesions is used. By visual examination of these images, there seem to be no common pattern that could be used to find and align the lesions within and between sessions. It is expected that the principal components of the original images could be useful during future...

  17. A Component Analysis of Marriage Enrichment.

    Science.gov (United States)

    Buston, Beverley G.; And Others

    Although marriage enrichment programs have been shown to be effective for many couples, a multidimensional approach to assessment is needed in investigating these groups. The components of information and social support in successful marriage enrichment programs were compared in a completely crossed 2 x 2 factorial design with repeated measures.…

  18. Active acoustic signals recognition of two kinds of stored grain pests based on Fast ICA%基于Fast ICA算法的2种储粮害虫活动声信号识别

    Institute of Scientific and Technical Information of China (English)

    张明真; 郭敏

    2012-01-01

    利用快速独立分量分析(fast independent component analysis,Fast ICA)算法,对混有高斯噪声的2种储粮害虫玉米象Sitophilus zeamais和赤拟谷盗Tribolium castaneum的活动声信号进行去噪,并使用Fast ICA算法识别和分离了2种储粮害虫爬行与翻身的4种活动声信号,证明了使用Fast ICA算法识别混合信号中每种害虫声信号的有效性和准确性.%Using fast independent component analysis algorithm, active acoustic signals mixed with gaussian noise of Sitophilus zeamais and Tribolium castaneum known as stored grain pests, were de-noised. Then Fast ICA algorithm were used to recognize and separate acoustic signals of four kinds of active acoustic signals, such as creeping and vibratory signals of Sitophilus zeamais and Tribolium castaneum. The results demonstrate the validity and accuracy to recognize each pest's acoustic signal from mixed signals by Fast ICA algorithm.

  19. DESAIN SILABUS MATRIKULASI BAHASA ARAB PMIAI ICAS-PARAMADINA JAKARTA

    Directory of Open Access Journals (Sweden)

    Mauidlotunnisa Mauidlotunnisa

    2014-06-01

    Full Text Available The aim of this research is to design a Arabic theaching syllabus for students at PMIAI ICASParamadina Jakarta that focused on Islamic philosophy and mysticism. The syllabus is an integrated syllabus which combines topics and academic reading skill based on Arabic for Academic Purposes. The process of syllabus design began with need analysis both internally and externally under the qualitative and quantitativ method. Based on the research findings, it is known that students in PMIAI ICAS-Paramadina Jakarta need an Arabic matriculation for reading and understanding texts since their Arabic proficiency level has not been adequate yet to conduct reading and understanding Islamic philosopy and mysticism texts in Arabic. DOI: 10.15408/a.v1i1.1132

  20. 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.

  1. FUZZY PRINCIPAL COMPONENT ANALYSIS AND ITS KERNEL BASED MODEL

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Principal Component Analysis (PCA) is one of the most important feature extraction methods, and Kernel Principal Component Analysis (KPCA) is a nonlinear extension of PCA based on kernel methods. In real world, each input data may not be fully assigned to one class and it may partially belong to other classes. Based on the theory of fuzzy sets, this paper presents Fuzzy Principal Component Analysis (FPCA) and its nonlinear extension model, i.e., Kernel-based Fuzzy Principal Component Analysis (KFPCA). The experimental results indicate that the proposed algorithms have good performances.

  2. Independent component analysis based on adaptive artificial bee colony

    National Research Council Canada - National Science Library

    Shi Zhang; Chao-Wei Bao; Hai-Bin Shen

    2016-01-01

    .... An independent component analysis method based on adaptive artificial bee colony algorithm is proposed in this paper, aiming at the problems of slow convergence and low computational precision...

  3. Quantifying functional connectivity in multi-subject fMRI data using component models

    DEFF Research Database (Denmark)

    Madsen, Kristoffer Hougaard; Churchill, Nathan William; Mørup, Morten

    2017-01-01

    -generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling...

  4. 基于ICA的X射线医学图像目标提取%Object Separation from Medical X-Ray Images Based on ICA

    Institute of Scientific and Technical Information of China (English)

    李艳; 喻春雨; 缪亚健; 费彬; 庄凤云

    2015-01-01

    X 射线医学成像能观察到患者体内病变组织,对医学诊断有重要参考价值。针对传统医学 X 射线图像噪声强、层次感差和器官组织重叠的问题,提出利用多能谱 X 射线成像结合独立成分分析(independent component analysis,ICA)进行图像去噪和目标提取。首先 ICA 结合稀疏编码收缩法对图像降噪预处理以保证目标提取精度;然后根据图像中各目标组成特性,分离图像中每个像素对应的目标厚度矩阵;最后 ICA以盲分离理论获得收敛矩阵重建出目标对象。在 ICA 算法中,借助于主观评价标准,发现当收敛次数大于40时目标分离成功;当幅值尺度在[25,45]区间内,目标图像对比度高且失真较小。同时,通过观测实验得到的三维峰值信噪比图表明:ICA 算法中收敛次数和幅值对图像质量有较大影响,当重建图像的对比度和边缘信息均达到较好效果时,收敛次数与幅值为85和35。%X-ray medical image can examine diseased tissue of patients and has important reference value for medical diagnosis. With the problems that traditional X-ray images have noise,poor level sense and blocked aliasing organs,this paper proposes a method for the introduction of multi-spectrum X-ray imaging and independent component analysis (ICA)algorithm to separate the target object.Firstly image de-noising preprocessing ensures the accuracy of target extraction based on independent compo-nent analysis and sparse code shrinkage.Then according to the main proportion of organ in the images,aliasing thickness matrix of each pixel was isolated.Finally independent component analysis obtains convergence matrix to reconstruct the target object with blind separation theory.In the ICA algorithm,it found that when the number is more than 40,the target objects separate successfully with the aid of subjective evaluation standard.And when the amplitudes of the scale are in the [25

  5. 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

  6. Agronomic characterization variety Quebranta in the Ica region, Peru

    Directory of Open Access Journals (Sweden)

    Cáceres Yparraguirre Hanna

    2015-01-01

    Full Text Available The study was to identify the best strains of the Quebranta variety cultivated in Peruvian region of Ica during campaigns 2011 to 2014. The evaluations were conducted in fourteen vineyards and the criteria to evaluate each one of them was that the same owner vineyard, would identify the best strain Quebranta for their good performance and sanitary quality. Productive parameters as grape weight and number of bunches per vine, average cluster weight, length and width of cluster and Berry weight were evaluated. Within the parameters of vegetative growth was assessed Ravaz index and as parameter for the composition of the grape was evaluated the concentration soluble solids (°Brix, total acidity and pH. Four phenological stages were recorded and defined from observed events in the branch of the year: Phase I comprising bud of winter to sprouting; Phase II of sprouting to full bloom; Phase III of full bloom to veraison and Phase IV of veraison to maturity. At the time of fruit setting were taken leaf samples to assess the State of health of each strain to Grapevine fanleaf virus, Grapevine fleck virus, Grapevine leafroll virus 1, Grapevine leafroll virus 3 and Tomato ringspot virus. The variables average bunch weight (22%, berry weight (20%, bunch length (13% and bunch width (11% presented the lowest values coefficient of variation. The variables of weight of grape per vine (52%, number of bunch (42% and index of Ravaz (60% has the highest values of coefficient of variation. Four variables were used that showed lower values (25% coefficient of variation for the weighted average. The variables that presented perfect correlation were berry weight and width of bunch, berry weight and Ravaz index, length of bunch and Ravaz index. The analysis of conglomerate allowed to group the strains in study in two groups which showed a significant difference between them (p < 0.0001. The principal component analysis identified that the variable of weight per bunch

  7. Columbia River Component Data Gap Analysis

    Energy Technology Data Exchange (ETDEWEB)

    L. C. Hulstrom

    2007-10-23

    This Data Gap Analysis report documents the results of a study conducted by Washington Closure Hanford (WCH) to compile and reivew the currently available surface water and sediment data for the Columbia River near and downstream of the Hanford Site. This Data Gap Analysis study was conducted to review the adequacy of the existing surface water and sediment data set from the Columbia River, with specific reference to the use of the data in future site characterization and screening level risk assessments.

  8. 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.

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

    Science.gov (United States)

    Feis, Rogier A.; Smith, Stephen M.; Filippini, Nicola; Douaud, Gwenaëlle; Dopper, Elise G. P.; Heise, Verena; Trachtenberg, Aaron J.; van Swieten, John C.; van Buchem, Mark A.; Rombouts, Serge A. R. B.; Mackay, Clare E.

    2015-01-01

    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 three 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 significant. 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. PMID:26578859

  10. 独立分量分析在伤口感染监测电子鼻技术中的应用%Independent Component Analysis for Wound Infection Using Electronic Nose Technology

    Institute of Scientific and Technical Information of China (English)

    徐姗; 田逢春; 杨先一; 闫嘉; 冯敬伟

    2011-01-01

    A method based on the electronic nose(e-nose)and independent component analysis( ICA) is presented to solve the time-consuming and complicated operation which appeared in traditional diagnosis method of wound infection. The gas sensor array of this e-nose consists of six metal oxide semiconductor sensors,which respond to the seven common pathogens in wound infection. The KBF( Radius Basis Function ) neural network is used for pattern recognition after pre-processing of the ICA. The results show that pre-processing of the gas sensor array measurement data by the ICA can simplify the structure of neural network, with the computation complexity reduced and the recognition accuracy of the wound infection pathogens increased.%针对传统的伤口感染诊断方法耗时长,操作复杂等问题,提出了一种基于电子鼻和独立分量分析(ICA)的方法来检测常见的伤口感染痛原菌.该电子鼻的传感器阵列由6个金属氧化物半导体传感器组成,分别对七种常见病原菌产生响应,然后利用RBF神经网络对经ICA预处理后的数据进行识别.结果表明,ICA对气体传感器阵列测量数据进行预处理,可以简化神经网络的结构,减少计算量,并能提高伤口感染病原菌识别的准确率.

  11. PRINCIPAL COMPONENT ANALYSIS - A POWERFUL TOOL IN COMPUTING MARKETING INFORMATION

    National Research Council Canada - National Science Library

    Cristinel Constantin

    2014-01-01

    ... that need to solve the marketing problem a company face with. The literature stresses the need to avoid the multicollinearity phenomenon in multivariate analysis and the features of Principal Component Analysis (PCA...

  12. Missing values in multi-level simultaneous component analysis

    NARCIS (Netherlands)

    Josse, Julie; Timmerman, Marieke E.; Kiers, Henk A. L.

    2013-01-01

    Component analysis of data with missing values is often performed with algorithms of iterative imputation. However, this approach is prone to overfitting problems. As an alternative, Josse et al. (2009) proposed a regularized algorithm in the framework of Principal Component Analysis (PCA). Here we

  13. Principal component analysis networks and algorithms

    CERN Document Server

    Kong, Xiangyu; Duan, Zhansheng

    2017-01-01

    This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

  14. Blind source separation dependent component analysis

    CERN Document Server

    Xiang, Yong; Yang, Zuyuan

    2015-01-01

    This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.

  15. Kernel principal component analysis for change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Morton, J.C.

    2008-01-01

    region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

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

    Science.gov (United States)

    Laforce, Robert; Tosun, Duygu; Ghosh, Pia; Lehmann, Manja; Madison, Cindee M; Weiner, Michael W; Miller, Bruce L; Jagust, William J; Rabinovici, Gil D

    2014-01-01

    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 ((11)C)-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 associated with

  17. 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

  18. Functional Network Overlap as Revealed by fMRI Using sICA and Its Potential Relationships with Functional Heterogeneity, Balanced Excitation and Inhibition, and Sparseness of Neuron Activity

    Science.gov (United States)

    Xu, Jiansong; Calhoun, Vince D.; Worhunsky, Patrick D.; Xiang, Hui; Li, Jian; Wall, John T.; Pearlson, Godfrey D.; Potenza, Marc N.

    2015-01-01

    Functional magnetic resonance imaging (fMRI) studies traditionally use general linear model-based analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-related modulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively across most or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICA might relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation. PMID:25714362

  19. Functional network overlap as revealed by fMRI using sICA and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity.

    Science.gov (United States)

    Xu, Jiansong; Calhoun, Vince D; Worhunsky, Patrick D; Xiang, Hui; Li, Jian; Wall, John T; Pearlson, Godfrey D; Potenza, Marc N

    2015-01-01

    Functional magnetic resonance imaging (fMRI) studies traditionally use general linear model-based analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-related modulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively across most or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICA might relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.

  20. Principal component analysis using neural network

    Institute of Scientific and Technical Information of China (English)

    杨建刚; 孙斌强

    2002-01-01

    The authors present their analysis of the differential equation dX ( t )/dt = AX ( t ) - XT( t ) BX( t)X( t), where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix,X E Rn ; showing that the equation characterizes a class of continuous type full-feedback artificial neural network; We give the analytic expression of the solution; discuss its asymptotic behavior; and finally present the result showing that, in almost all cases, one and only one of following eases is true. 1. For any initial value X0∈Rn, the solution approximates asymptotically to zero vector. In thin cane, the real part of each eigenvalue of A is non-positive. 2. For any initial value X0 outside a proper subspace of Rn,the solution approximates asymptoticaUy to a nontrivial constant vector Y( X0 ). In this cane, the eigenvalue of A with maximal real part is the positive number λ=Ⅱ Y (X0)ⅡB2 and Y (X0) is the corre-sponding eigenvector. 3. For any initial value X0 outsidea proper subspace of Rn, the solution approximates asymptotically to a non-constant periodic function Y( X0 , t ). Then the eigenvalues of A with maximal real part is a pair of conjugate complex numbers which can be computed.

  1. Principal component analysis using neural network

    Institute of Scientific and Technical Information of China (English)

    杨建刚; 孙斌强

    2002-01-01

    The authors present their analysis of the differential equation dX(t)/dt=AX(t)-XT(t)BX(t)X(t), where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix, X∈Rn; showing that the equation characterizes a class of continuous type full-feedback artificial neural network; We give the analytic expression of the solution; discuss its asymptotic behavior; and finally present the result showing that, in almost all cases, one and only one of following cases is true. 1. For any initial value X0∈Rn, the solution approximates asymptotically to zero vector. In this case, the real part of each eigenvalue of A is non-positive. 2. For any initial value X0 outside a proper subspace of Rn, the solution approximates asymptotically to a nontrivial constant vector (X0). In this case, the eigenvalue of A with maximal real part is the positive number λ=‖(X0)‖2B and (X0) is the corresponding eigenvector. 3. For any initial value X0 outside a proper subspace of Rn, the solution approximates asymptotically to a non-constant periodic function (X0,t). Then the eigenvalues of A with maximal real part is a pair of conjugate complex numbers which can be computed.

  2. 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

    2015-11-15

    Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is still 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. This 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 from SAD with manic episodes (SADM), and 13 patients suffering from 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 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 included 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 that 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

  3. Exploration of Shape Variation Using Localized Components Analysis

    OpenAIRE

    Alcantara, Dan A; Carmichael, Owen; Harcourt-Smith, Will; Sterner, Kirstin; Frost, Stephen R.; Dutton, Rebecca; Thompson, Paul; Delson, Eric; Amenta, Nina

    2009-01-01

    Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive p...

  4. The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer's Disease and bvFTD.

    Science.gov (United States)

    Tuovinen, Timo; Rytty, Riikka; Moilanen, Virpi; Abou Elseoud, Ahmed; Veijola, Juha; Remes, Anne M; Kiviniemi, Vesa J

    2016-01-01

    Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

  5. Dynamic Modal Analysis of Vertical Machining Centre Components

    Directory of Open Access Journals (Sweden)

    Anayet U. Patwari

    2009-01-01

    Full Text Available 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 and analyzed by finite element simulation using ABAQUS software to extract the different theoretical mode shape of the components. The model is evaluated and corrected with experimental results by modal testing of the machine components in which the natural frequencies and the shape of vibration modes are analyzed. The analysis resulted in determination of the direction of the maximal compliance of a particular machine component.

  6. 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

  7. 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.

  8. Engine structures analysis software: Component Specific Modeling (COSMO)

    Science.gov (United States)

    McKnight, R. L.; Maffeo, R. J.; Schwartz, S.

    1994-08-01

    A component specific modeling software program has been developed for propulsion systems. This expert program is capable of formulating the component geometry as finite element meshes for structural analysis which, in the future, can be spun off as NURB geometry for manufacturing. COSMO currently has geometry recipes for combustors, turbine blades, vanes, and disks. Component geometry recipes for nozzles, inlets, frames, shafts, and ducts are being added. COSMO uses component recipes that work through neutral files with the Technology Benefit Estimator (T/BEST) program which provides the necessary base parameters and loadings. This report contains the users manual for combustors, turbine blades, vanes, and disks.

  9. 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

  10. Component

    Directory of Open Access Journals (Sweden)

    Tibor Tot

    2011-01-01

    Full Text Available A unique case of metaplastic breast carcinoma with an epithelial component showing tumoral necrosis and neuroectodermal stromal component is described. The tumor grew rapidly and measured 9 cm at the time of diagnosis. No lymph node metastases were present. The disease progressed rapidly and the patient died two years after the diagnosis from a hemorrhage caused by brain metastases. The morphology and phenotype of the tumor are described in detail and the differential diagnostic options are discussed.

  11. Combining fMRI and SNP Data to Investigate Connections Between Brain Function and Genetics Using Parallel ICA

    Science.gov (United States)

    Liu, Jingyu; Pearlson, Godfrey; Windemuth, Andreas; Ruano, Gualberto; Perrone-Bizzozero, Nora I.; Calhoun, Vince

    2009-01-01

    There is current interest in understanding genetic influences on both healthy and disordered brain function. We assessed brain function with functional magnetic resonance imaging (fMRI) data collected during an auditory oddball task—detecting an infrequent sound within a series of frequent sounds. Then, task-related imaging findings were utilized as potential intermediate phenotypes (endophenotypes) to investigate genomic factors derived from a single nucleotide polymorphism (SNP) array. Our target is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data. The method was aimed to identify simultaneously independent components of each modality and the relationships between them. When 43 healthy controls and 20 schizophrenia patients, all Caucasian, were studied, we found a correlation of 0.38 between one fMRI component and one SNP component. This fMRI component consisted mainly of parietal lobe activations. The relevant SNP component was contributed to significantly by 10 SNPs located in genes, including those coding for the nicotinic α-7cholinergic receptor, aromatic amino acid decarboxylase, disrupted in schizophrenia 1, among others. Both fMRI and SNP components showed significant differences in loading parameters between the schizophrenia and control groups (P = 0.0006 for the fMRI component; P = 0.001 for the SNP component). In summary, we constructed a framework to identify interactions between brain functional and genetic information; our findings provide a proof-of-concept that genomic SNP factors can be investigated by using endophenotypic imaging findings in a multivariate format. PMID:18072279

  12. Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA.

    Directory of Open Access Journals (Sweden)

    Stéphane Sockeel

    Full Text Available Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1 An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2 A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.

  13. Principal component analysis of minimal excitatory postsynaptic potentials.

    Science.gov (United States)

    Astrelin, A V; Sokolov, M V; Behnisch, T; Reymann, K G; Voronin, L L

    1998-02-20

    'Minimal' excitatory postsynaptic potentials (EPSPs) are often recorded from central neurones, specifically for quantal analysis. However the EPSPs may emerge from activation of several fibres or transmission sites so that formal quantal analysis may give false results. Here we extended application of the principal component analysis (PCA) to minimal EPSPs. We tested a PCA algorithm and a new graphical 'alignment' procedure against both simulated data and hippocampal EPSPs. Minimal EPSPs were recorded before and up to 3.5 h following induction of long-term potentiation (LTP) in CA1 neurones. In 29 out of 45 EPSPs, two (N=22) or three (N=7) components were detected which differed in latencies, rise time (Trise) or both. The detected differences ranged from 0.6 to 7.8 ms for the latency and from 1.6-9 ms for Trise. Different components behaved differently following LTP induction. Cases were found when one component was potentiated immediately after tetanus whereas the other with a delay of 15-60 min. The immediately potentiated component could decline in 1-2 h so that the two components contributed differently into early (reflections of synchronized quantal releases. In general, the results demonstrate PCA applicability to separate EPSPs into different components and its usefulness for precise analysis of synaptic transmission.

  14. Outliers detection in multivariate time series by independent component analysis.

    Science.gov (United States)

    Baragona, Roberto; Battaglia, Francesco

    2007-07-01

    In multivariate time series, outlying data may be often observed that do not fit the common pattern. Occurrences of outliers are unpredictable events that may severely distort the analysis of the multivariate time series. For instance, model building, seasonality assessment, and forecasting may be seriously affected by undetected outliers. The structure dependence of the multivariate time series gives rise to the well-known smearing and masking phenomena that prevent using most outliers' identification techniques. It may be noticed, however, that a convenient way for representing multiple outliers consists of superimposing a deterministic disturbance to a gaussian multivariate time series. Then outliers may be modeled as nongaussian time series components. Independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice, independent component analysis may be used to analyze multivariate observable time series and separate regular and outlying unobservable components. In the factor models framework too, it is shown that independent component analysis is a useful tool for detection of outliers in multivariate time series. Some algorithms that perform independent component analysis are compared. It has been found that all algorithms are effective in detecting various types of outliers, such as patches, level shifts, and isolated outliers, even at the beginning or the end of the stretch of observations. Also, there is no appreciable difference in the ability of different algorithms to display the outlying observations pattern.

  15. Comparing the microvascular specificity of the 3 T and 7 T BOLD response using ICA and Susceptibility-Weighted Imaging

    Directory of Open Access Journals (Sweden)

    Alexander eGeissler

    2013-08-01

    Full Text Available In functional MRI it is desirable for the blood-oxygenation level dependent (BOLD signal to be localized to the tissue containing activated neurons rather than the veins draining that tissue. This study addresses the dependence of the specificity of the BOLD signal – the relative contribution of the BOLD signal arising from tissue compared to venous vessels – on magnetic field strength. To date, studies of specificity have been based on models or indirect measures of BOLD sensitivity such as signal to noise ratio and relaxation rates, and assessment has been made in isolated vein and tissue voxels. The consensus has been that ultra high field systems not only significantly increase BOLD sensitivity but also specificity, that is, there is a proportionately reduced signal contribution from draining veins. Specificity was not quantified in prior studies, however, due to the difficulty of establishing a reliable network of veins in the activated volume. In this study we use a map of venous vessel networks extracted from 7 T high resolution Susceptibility Weighted Images (SWI to quantify the relative contributions of micro- and macrovasculature to functional MRI (fMRI results obtained at 3 T and 7 T. High resolution measurements made here minimize the contribution of physiological noise and Independent Component Analysis (ICA is used to separate activation from technical, physiological and motion artifacts. ICA also avoids the possibility of timing-dependent bias from different micro- and macrovasculature responses. We find a significant increase in the number of activated voxels at 7 T in both the veins and the microvasculature – a BOLD sensitivity increase - with the increase in the microvasculature being higher. However, the small increase in sensitivity at 7 T was not significant. For the experimental conditions of this study, our findings do not support the hypothesis of an increased specificity of the BOLD response at ultra-high field.

  16. 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.

  17. PRINCIPAL COMPONENT ANALYSIS IN APPLICATION TO OBJECT ORIENTATION

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    This paper proposes a new method based on principal component analysis to find the direction of an object in any pose.Experiments show that this method is fast,can be applied to objects with any pixel distribution and keep the original properties of objects invariant.It is a new application of PCA in image analysis.

  18. 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...

  19. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  20. Three-way component analysis : Principles and illustrative application

    NARCIS (Netherlands)

    Kiers, Henk A.L.; Van Mechelen, Iven

    2001-01-01

    Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for example, when data are collected on individuals, in different settings, and on different measures. Such techniques summarize all information in a 3-way data set by summarizing, for each way of the 3-way

  1. 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

  2. Impact of severe extracranial ICA stenosis on MRI perfusion and diffusion parameters in acute ischemic stroke

    Directory of Open Access Journals (Sweden)

    Philipp eKaesemann

    2014-12-01

    Full Text Available Purpose:The aim of this study was to investigate the impact of a coexisting internal carotid artery (ICA stenosis on lesion volumes as well as diffusion and perfusion parameters in acute ischemic stroke resulting from middle cerebral artery (MCA occlusion.Material and Methods:MRI data of 32 patients with MCA occlusion with or without additional ICA stenosis imaged within 4.5 hours of symptom onset were analyzed. Both groups consisted of 16 patients. Acute diffusion lesions were semi-automatically segmented in apparent diffusion coefficient (ADC MRI datasets. Perfusion maps of cerebral blood volume (CBV, cerebral blood flow (CBF, mean transit time (MTT and Tmax were calculated using perfusion-weighted MRI datasets. Tissue-at-risk (TAR volumes were generated by subtracting the ADC lesion from the hypoperfusion lesion defined by Tmax >6s. Median ADC and perfusion parameter values were extracted separately for the diffusion lesion and tissue-at-risk and used for statistical analysis.Results:No significant differences were found between the groups regarding the diffusion lesion and tissue-at-risk volumes. Statistical analysis of diffusion and perfusion parameters revealed CBV as the only parameter with a significant difference (p=0.009 contributing a small effect (ɛ²=0.11 to the group comparison with higher CBV values for the patient group with a coexisting ICA stenosis, while no significant effects were found for the other diffusion and perfusion parameters analyzed.Conclusion:The results of this study suggest that a coexisting ICA stenosis does not have a strong effect on tissue status or perfusion parameters in acute stroke patients except for a moderate elevation of CBV. This may reflect improved collateral circulation or ischemic preconditioning in patients with a pre-existing proximal stenosis balancing impaired perfusion from the stenosis.

  3. A principal component analysis of transmission spectra of wine distillates

    Science.gov (United States)

    Rogovaya, M. V.; Sinitsyn, G. V.; Khodasevich, M. A.

    2014-11-01

    A chemometric method of decomposing multidimensional data into a small-sized space, the principal component method, has been applied to the transmission spectra of vintage Moldovan wine distillates. A sample of 42 distillates aged from four to 7 years from six producers has been used to show the possibility of identifying a producer in a two-dimensional space of principal components describing 94.5% of the data-matrix dispersion. Analysis of the loads into the first two principal components has shown that, in order to measure the optical characteristics of the samples under study using only two wavelengths, it is necessary to select 380 and 540 nm, instead of the standard 420 and 520 nm, to describe the variability of the distillates by one principal component or 370 and 520 nm to describe the variability by two principal components.

  4. The application of Principal Component Analysis to materials science data

    Directory of Open Access Journals (Sweden)

    Changwon Suh

    2006-01-01

    Full Text Available The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the microscopic characteristics of materials on their macroscopic or engineering behavior. In this paper we demonstrate the value of principal component analysis of property data associated with high temperature superconductivity to examine the statistical impact of the materials' intrinsic characteristics on high temperature superconducting behavior

  5. Reliability Analysis of Fatigue Fracture of Wind Turbine Drivetrain Components

    DEFF Research Database (Denmark)

    Berzonskis, Arvydas; Sørensen, John Dalsgaard

    2016-01-01

    in the volume of the casted ductile iron main shaft, on the reliability of the component. The probabilistic reliability analysis conducted is based on fracture mechanics models. Additionally, the utilization of the probabilistic reliability for operation and maintenance planning and quality control is discussed....... of operation and maintenance. The manufacturing of casted drivetrain components, like the main shaft of the wind turbine, commonly result in many smaller defects through the volume of the component with sizes that depend on the manufacturing method. This paper considers the effect of the initial defect present...

  6. Exploration of shape variation using localized components analysis.

    Science.gov (United States)

    Alcantara, Dan A; Carmichael, Owen; Harcourt-Smith, Will; Sterner, Kirstin; Frost, Stephen R; Dutton, Rebecca; Thompson, Paul; Delson, Eric; Amenta, Nina

    2009-08-01

    Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

  7. Alterations of Gray and White Matter Networks in Patients with Obsessive-Compulsive Disorder: A Multimodal Fusion Analysis of Structural MRI and DTI Using mCCA+jICA.

    Directory of Open Access Journals (Sweden)

    Seung-Goo Kim

    Full Text Available Many of previous neuroimaging studies on neuronal structures in patients with obsessive-compulsive disorder (OCD used univariate statistical tests on unimodal imaging measurements. Although the univariate methods revealed important aberrance of local morphometry in OCD patients, the covariance structure of the anatomical alterations remains unclear. Motivated by recent developments of multivariate techniques in the neuroimaging field, we applied a fusion method called "mCCA+jICA" on multimodal structural data of T1-weighted magnetic resonance imaging (MRI and diffusion tensor imaging (DTI of 30 unmedicated patients with OCD and 34 healthy controls. Amongst six highly correlated multimodal networks (p < 0.0001, we found significant alterations of the interrelated gray and white matter networks over occipital and parietal cortices, frontal interhemispheric connections and cerebella (False Discovery Rate q ≤ 0.05. In addition, we found white matter networks around basal ganglia that correlated with a subdimension of OC symptoms, namely 'harm/checking' (q ≤ 0.05. The present study not only agrees with the previous unimodal findings of OCD, but also quantifies the association of the altered networks across imaging modalities.

  8. Alterations of Gray and White Matter Networks in Patients with Obsessive-Compulsive Disorder: A Multimodal Fusion Analysis of Structural MRI and DTI Using mCCA+jICA.

    Science.gov (United States)

    Kim, Seung-Goo; Jung, Wi Hoon; Kim, Sung Nyun; Jang, Joon Hwan; Kwon, Jun Soo

    2015-01-01

    Many of previous neuroimaging studies on neuronal structures in patients with obsessive-compulsive disorder (OCD) used univariate statistical tests on unimodal imaging measurements. Although the univariate methods revealed important aberrance of local morphometry in OCD patients, the covariance structure of the anatomical alterations remains unclear. Motivated by recent developments of multivariate techniques in the neuroimaging field, we applied a fusion method called "mCCA+jICA" on multimodal structural data of T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) of 30 unmedicated patients with OCD and 34 healthy controls. Amongst six highly correlated multimodal networks (p < 0.0001), we found significant alterations of the interrelated gray and white matter networks over occipital and parietal cortices, frontal interhemispheric connections and cerebella (False Discovery Rate q ≤ 0.05). In addition, we found white matter networks around basal ganglia that correlated with a subdimension of OC symptoms, namely 'harm/checking' (q ≤ 0.05). The present study not only agrees with the previous unimodal findings of OCD, but also quantifies the association of the altered networks across imaging modalities.

  9. 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.

  10. Staphylococcus aureus autoinducer-2 quorum sensing decreases biofilm formation in an icaR-dependent manner

    Directory of Open Access Journals (Sweden)

    Yu Dan

    2012-12-01

    Full Text Available Abstract Background Staphylococcus aureus is an important pathogen that causes biofilm-associated infection in humans. Autoinducer 2 (AI-2, a quorum-sensing (QS signal for interspecies communication, has a wide range of regulatory functions in both Gram-positive and Gram-negative bacteria, but its exact role in biofilm formation in S. aureus remains unclear. Results Here we demonstrate that mutation of the AI-2 synthase gene luxS in S. aureus RN6390B results in increased biofilm formation compared with the wild-type (WT strain under static, flowing and anaerobic conditions and in a mouse model. Addition of the chemically synthesized AI-2 precursor in the luxS mutation strain (ΔluxS restored the WT phenotype. Real-time RT-PCR analysis showed that AI-2 activated the transcription of icaR, a repressor of the ica operon, and subsequently a decreased level of icaA transcription, which was presumably the main reason why luxS mutation influences biofilm formation. Furthermore, we compared the roles of the agr-mediated QS system and the LuxS/AI-2 QS system in the regulation of biofilm formation using the ΔluxS strain, RN6911 and the Δagr ΔluxS strain. Our data indicate a cumulative effect of the two QS systems on the regulation of biofilm formation in S. aureus. Conclusion These findings demonstrate that AI-2 can decrease biofilm formation in S. aureus via an icaR-activation pathway. This study may provide clues for therapy in S. aureus biofilm-associated infection.

  11. Autoinducer-2 increases biofilm formation via an ica- and bhp-dependent manner in Staphylococcus epidermidis RP62A.

    Science.gov (United States)

    Xue, Ting; Ni, Jingtian; Shang, Fei; Chen, Xiaolin; Zhang, Ming

    2015-05-01

    Staphylococcus epidermidis has become the most common cause of nosocomial bacteraemia and the principal organism responsible for indwelling medical device -associated infections. Its pathogenicity is mainly due to its ability to form biofilms on the implanted medical devices. Biofilm formation is a quorum-sensing (QS)-dependent process controlled by autoinducers, which are signalling molecules. Here, we investigated the function of the autoinducer-2 (AI-2) QS system, especially the influence of AI-2 on biofilm formation in S. epidermidis RP62A. Results showed that the addition of AI-2 leads to a significant increase in biofilm formation, in contrast with previous studies which showed that AI-2 limits biofilm formation in Staphylococci. We found that AI-2 increases biofilm formation by enhancing the transcription of the ica operon, which is a known component in the AI-2-regulated biofilm pathway. In addition, we first observed that the transcript level of bhp, which encodes a biofilm-associated protein, was also increased following the addition of AI-2. Furthermore, we found that, among the known biofilm regulator genes (icaR, sigB, rbsU, sarA, sarX, sarZ, clpP, agrA, abfR, arlRS, saeRS), only icaR can be regulated by AI-2, suggesting that AI-2 may regulate biofilm formation by an icaR-dependent mechanism in S. epidermidis RP62A.

  12. The Effects of Overextraction on Factor and Component Analysis.

    Science.gov (United States)

    Fava, J L; Velicer, W F

    1992-07-01

    The effects of overextracting factors and components within and between the methods of maximum likelihood factor analysis (MLFA) and principal component analysis (PCA) were examined. Computer-simulated data sets were generated to represent a range of factor and component patterns. Saturation (aij = .8, .6 & .4), sample size (N = 75, 150,225,450), and variable-to-component (factor) ratio (p:m = 12:1,6:1, & 4:1) were conditions manipulated. In Study 1, scores based on the incorrect patterns were correlated with correct scores within each method after each overextraction. In Study 2, scores were correlated between the methods of PCAand MLFA after each overextraction. Overextraction had a negative effect, but scores based on strong component and factor patterns displayed robustness to the effects of overextraction. Low item saturation and low sample size resulted in degraded score reproduction. Degradation was strongest for patterns that combined low saturation and low sample size. Component and factor scores were highly correlated even at maximal levels of overextraction. Dissimilarity between score methods was the greatest in conditions that combined low saturation and low sample size. Some guidelines for researchers concerning the effects of overextraction are noted, as well as some cautions in the interpretation of results.

  13. 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.

  14. 基于 ICA K-Means 的产品口碑演化聚类与营销分析%Clustering and Marketing Analysis for Products Online Word-of-mouth Activity Series Based on ICA K-Means

    Institute of Scientific and Technical Information of China (English)

    李红; 潘娜

    2016-01-01

    For product, online word-of-mouse activity is a very typical index, which reveals life cycle evolution model of product.Understanding the product life cycle helps decision makers to make marketing strategy.It is more difficult to do clustering analysis because the product online comments are high-dimensional and complex.Thus, combining K-means algorithm with independent comment analysis and clustering products by this algorithm can improve the accu-racy and reduce complexity in no small measure.Furthermore, in-depth analysis on the product life curve can effec-tively improve the effect of online word-of-mouth information in e-commerce marketing management and decision sup-port, deepening the research on online reputation activity.%对于产品而言,其在线口碑的活跃度是非常具有代表性的一个指标。在线口碑活跃度的高低,直接揭示产品的生命周期演化模式,对于产品生命周期有全面的了解有助于决策者制定营销计划以及战略。但由于产品在线评论的高维性和复杂性,使得其聚类的难度加大。所以,在普通的K均值算法的基础上引入独立成份分析,对异类产品之间或同类产品在线口碑的活跃度之间进行聚类分析,可以大大降低复杂性和提升聚类准确性;同时深入分析提取出的产品生命周期曲线,有效提升在线口碑信息在电子商务营销管理与决策支持中的作用,深化在线口碑活跃度的管理学视角研究。

  15. 基于ICA模型的国际股指期货及股票市场对我国股市波动溢出研究%Volatility Spillover from International Stock Index Futures and Spot Markets to Chinese Stock Market Based on ICA Model

    Institute of Scientific and Technical Information of China (English)

    柴尚蕾; 郭崇慧; 苏木亚

    2011-01-01

    Independent Component Analysis(ICA) is introduced to study volatility spillovers from financial derivative markets to basic markets. It remedies the deficiency of using traditional methods to solve high dimensional financial time series volatility problem in the past. By comparing with multivariate GARCH models, such as VECH, BEKK and DCC, ICA-EGARCH-M model in this paper shows some advantages of solving high dimensional problem. In empirical study, ICA-EGARCH-M model is employed to examine volatility spillovers effects from stock index futures and spot markets of the US, UK, Japan and Hongkong to Chinese stock market. The results show that the ICA-EGARCH-M model not only confirms that there exists volatility spillovers, but also reflects the main resource of volatility spillovers. It can better resolve volatility spillovers problem of high dimensional financial time series.%将独立成分分析(ICA)方法引入金融衍生品市场与基础市场之间的波动溢出研究,克服了传统方法解决高维金融时间序列波动问题时的障碍.通过与VECH、BEKK和DCC等传统多元GARCH模型的对比分析,本文所建立的ICA-EGARCH-M模型在解决高维问题时体现出一定的优势.在实证研究中,应用该模型考察了美国、英国、日本和中国香港的股指期货市场及其股票市场对我国股票市场的共同波动溢出.结果表明ICA-EGARCH-M模型不仅验证了波动溢出效应的存在,而且反映出了波动溢出的主要来源,能够较好地解决高维金融时间序列数据的波动溢出问题.

  16. 高光谱数据非监督分类的改进独立成分分析方法%An Improved Independent Component Analysis Method for Unsupervised Classification of Hyperspectral Data

    Institute of Scientific and Technical Information of China (English)

    李娜; 赵慧洁

    2011-01-01

    To solve the problem that the first - order and second - order statistics may be inadequate for obtaining a complete representation of the data, a high - order statistics - based method, kurtosis - based independent component analysis (KICA), is introduced to implement unsupervised classification of hyperspectral data. Aimed at the purpose that kurtosis can be very sensitive to outliers such as noise, the improved KICA (IKICA) model is proposed in the work when kurtosis is used as optimization criterion for the ICA problem. To evaluate the performance of the proposed algorithm and its application capability in unsupervised classification, IKICA is compared with maximum likelihood -based ICA and negentropy -based ICA, and the synthesized and real hyperspectral data acquired by Object Modularization Imaging Spectrometer (OMIS) and Pushbroom Hyperspectral Imager (PHI) are used. The results show that convergence speed and robustness are enhanced obviously and anti - noise capability is improved in the authors' work. The application result has high precision of classification.%利用数据本身统计特性是实现高光谱数据非监督分类的有效方法之一.针对利用高光谱数据一阶、二阶统计量不能完全表征数据结构的问题,提出了一种基于数据高阶统计特性--峭度的改进独立成分分析方法(Improved Kurtosis-Based Independent Component Analysis,IKICA)的高光谱数据非监督分类方法,并针对利用峭度进行非高斯性度量时对噪声等敏感的问题进行了模型改进.利用同一航带的OMIS高光谱遥感数据对该算法的性能进行了评价,并分别与基于最大似然估计和基于负熵的独立成分分析(ICA)方法进行了性能比较.将该方法应用于PHI获取的方麓茶场航空高光谱数据的非监督分类,结果表明,本文提出的算法明显地提高了运算的收敛速度和鲁棒性,并具有较高的分类精度和较强的抗噪声能力.

  17. 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 geometry, material properties and fixed point characteristics to calculate the dimensions and subsequent feasibility of any architectural design. The proposed conceptual design tool provides the possibility for the architect to work with both the aesthetic as well as the structural aspects of architecture...... with a static determinate roof structure modelled by beam components is given. The example outlines the idea of the tool for conceptual design in early phase of a multidisciplinary design process between architecture and structural engineering....

  18. 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...... without jumping from aesthetics to structural digital design tools and back, but to work with both simultaneously and real time. The engineering level of knowledge is incorporated at a conceptual thinking level, i.e. qualitative information is used in stead of using quantitative information. An example...... with a static determinate roof structure modelled by beam components is given. The example outlines the idea of the tool for conceptual design in early phase of a multidisciplinary design process between architecture and structural engineering....

  19. Analysis methods for structure reliability of piping components

    Energy Technology Data Exchange (ETDEWEB)

    Schimpfke, T.; Grebner, H.; Sievers, J. [Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS) mbH, Koeln (Germany)

    2004-07-01

    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.)

  20. Automated Kernel Independent Component Analysis Based Two Variable Weighted Multi-view Clustering for Complete and Incomplete Dataset

    Directory of Open Access Journals (Sweden)

    M. Kalaiarasu

    2015-04-01

    Full Text Available In recent years, data are collected to a greater extent from several sources or represented by multiple views, in which different views express different point of views of the data. Even though each view might be individually exploited for discovering patterns by clustering, the clustering performance could be further perfect by exploring the valuable information among multiple views. On the other hand, several applications offer only a partial mapping among the two levels of variables such as the view weights and the variables weights views, developing a complication for current approaches, since incomplete view of the data are not supported by these approaches. In order to overcome this complication, proposed a Kernel-based Independent Component Analysis (KICA based on steepest descent subspace two variables weighted clustering in this study and it is named as KICASDSTWC that can execute with an incomplete mapping. Independent Component Analysis (ICA which exploit distinguish operations depending on canonical correlations in a reproducing kernel Hilbert space. Centroid values of the subspace clustering approaches are optimized depending on steepest descent algorithm and Artificial Fish Swarm Optimization (AFSO algorithm for the purpose of weight calculation to recognize the compactness of the view and a variable weight. This framework permits the integration of complete and incomplete views of data. Experimental observations on three real-life data sets and the outcome have revealed that the proposed KICASDSTWC considerably outperforms all the competing approaches in terms of Precision, Recall, F Measure, Average Cluster Entropy (ACE and Accuracy for both complete and incomplete view of the data with respect to the true clusters in the data.

  1. Principal component analysis of image gradient orientations for face recognition

    NARCIS (Netherlands)

    Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data

  2. Sparse principal component analysis in hyperspectral change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Larsen, Rasmus; Vestergaard, Jacob Schack

    2011-01-01

    This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bi-temporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very ...

  3. The dynamics of on-line principal component analysis

    NARCIS (Netherlands)

    Biehl, M.; Schlösser, E.

    1998-01-01

    The learning dynamics of an on-line algorithm for principal component analysis is described exactly in the thermodynamic limit by means of coupled ordinary differential equations for a set of order parameters. It is demonstrated that learning is delayed significantly because existing symmetries amon

  4. Convergence of algorithms used for principal component analysis

    Institute of Scientific and Technical Information of China (English)

    张俊华; 陈翰馥

    1997-01-01

    The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrix A which is the expectation of observed random samples. The conditions required here are considerably weaker than those used in previous work.

  5. 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...

  6. Principal Component Analysis: Most Favourite Tool in Chemometrics

    Indian Academy of Sciences (India)

    Keshav Kumar

    2017-08-01

    Principal component analysis (PCA) is the most commonlyused chemometric technique. It is an unsupervised patternrecognition technique. PCA has found applications in chemistry,biology, medicine and economics. The present work attemptsto understand how PCA work and how can we interpretits results.

  7. [Component analysis on polysaccharides in exocarp of Ginkgo biloba].

    Science.gov (United States)

    Song, G; Xu, A; Chen, H; Wang, X

    1997-09-01

    This paper reports the content and component analysis on polysaccharides in exocarp of Ginkgo biloba. The results show that the content of total saccharides is 89.7%; content of polysaccharides is 84.6%; content of reductic saccharides is 5.1%; the polysaccharides are composed of glucose, fructose, galactose and rhamnose.

  8. How to perform multiblock component analysis in practice

    NARCIS (Netherlands)

    De Roover, Kim; Ceulemans, Eva; Timmerman, Marieke E.

    To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been measured for different groups of subjects, where the data for each group constitute a different data block), researchers have a variety of multiblock component analysis and

  9. 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

  10. Analysis of Variance Components for Genetic Markers with Unphased Genotypes.

    Science.gov (United States)

    Wang, Tao

    2016-01-01

    An ANOVA type general multi-allele (GMA) model was proposed in Wang (2014) on analysis of variance components for quantitative trait loci or genetic markers with phased or unphased genotypes. In this study, by applying the GMA model, we further examine estimation of the genetic variance components for genetic markers with unphased genotypes based on a random sample from a study population. In one locus and two loci cases, we first derive the least square estimates (LSE) of model parameters in fitting the GMA model. Then we construct estimators of the genetic variance components for one marker locus in a Hardy-Weinberg disequilibrium population and two marker loci in an equilibrium population. Meanwhile, we explore the difference between the classical general linear model (GLM) and GMA based approaches in association analysis of genetic markers with quantitative traits. We show that the GMA model can retain the same partition on the genetic variance components as the traditional Fisher's ANOVA model, while the GLM cannot. We clarify that the standard F-statistics based on the partial reductions in sums of squares from GLM for testing the fixed allelic effects could be inadequate for testing the existence of the variance component when allelic interactions are present. We point out that the GMA model can reduce the confounding between the allelic effects and allelic interactions at least for independent alleles. As a result, the GMA model could be more beneficial than GLM for detecting allelic interactions.

  11. ECG signals denoising using wavelet transform and independent component analysis

    Science.gov (United States)

    Liu, Manjin; Hui, Mei; Liu, Ming; Dong, Liquan; Zhao, Zhu; Zhao, Yuejin

    2015-08-01

    A method of two channel exercise electrocardiograms (ECG) signals denoising based on wavelet transform and independent component analysis is proposed in this paper. First of all, two channel exercise ECG signals are acquired. We decompose these two channel ECG signals into eight layers and add up the useful wavelet coefficients separately, getting two channel ECG signals with no baseline drift and other interference components. However, it still contains electrode movement noise, power frequency interference and other interferences. Secondly, we use these two channel ECG signals processed and one channel signal constructed manually to make further process with independent component analysis, getting the separated ECG signal. We can see the residual noises are removed effectively. Finally, comparative experiment is made with two same channel exercise ECG signals processed directly with independent component analysis and the method this paper proposed, which shows the indexes of signal to noise ratio (SNR) increases 21.916 and the root mean square error (MSE) decreases 2.522, proving the method this paper proposed has high reliability.

  12. 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.

  13. Principal Component Analysis - A Powerful Tool in Computing Marketing Information

    Directory of Open Access Journals (Sweden)

    Constantin C.

    2014-12-01

    Full Text Available This paper is about an instrumental research regarding a powerful multivariate data analysis method which can be used by the researchers in order to obtain valuable information for decision makers that need to solve the marketing problem a company face with. The literature stresses the need to avoid the multicollinearity phenomenon in multivariate analysis and the features of Principal Component Analysis (PCA in reducing the number of variables that could be correlated with each other to a small number of principal components that are uncorrelated. In this respect, the paper presents step-by-step the process of applying the PCA in marketing research when we use a large number of variables that naturally are collinear.

  14. Coordinated and uncoordinated design of LFO damping controllers with IPFC and PSS using ICA and SFLA

    Institute of Scientific and Technical Information of China (English)

    Mahdi Toupchi Khosroshahi; Farhad Mohajel Kazemi; Mohammad Reza Jannati Oskuee; Sajad Najafi-Ravadanegh

    2015-01-01

    amplitude and longer settling time compared with coordinated IPFC and PSS ICA-based controllers. This comparison shows that overshoots, undershoots and the settling times are reduced considerably in coordinated mode of IPFC based controller and PSS using ICA. Analysis of the system performance shows that the proposed method has excellent response to different faults in power system.

  15. Contributions of the IGU and ICA commissions in population studies.

    Science.gov (United States)

    Nag, P

    1989-01-01

    This paper surveys the contributions of the International Geographic Union (IGU) and the International Cartographic Association (ICA) to the field of population studies over the past 3 decades. Reviewing the various focal themes of conferences sponsored by the organizations since the 1960s, the author examines the evolution of population studies in IGU and ICA. During the 1960s, IGU began holding symposia addressing the issue of population pressure on the physical and social resource in developing countries. However, it wasn't until 1972, at a meeting in Edmonton, Canada, when IGU first addressed the issue of migration. But since then, migration has remained on the the key concerns of IGU. In 1978, the union hosted a symposium on Population Redistribution in Africa -- the first in a series of conferences focusing on the issue of migration. As an outgrowth of migration, the IGU also began addressing the related issue of population education. The interest in migration has continued through the 1980s. In addition to studies of regional migration, the IGU has also focused on conceptual issues such as migrant labor, environmental concerns, women and migration, and urbanization. In 1984, IGU began cooperating with ICA in the areas of census cartography and population cartography. The author concludes his review of IGU and ICA activities by discussing the emerging trends in population studies. The author foresees a more refined study of migration and more sophisticated population mapping, the result of better study techniques and the use of computer technology.

  16. Parasites of Periplaneta americana linnaeus "domestic cockroach" from Ica.

    Directory of Open Access Journals (Sweden)

    Mary Fernádez B.

    2014-06-01

    Full Text Available 244 specimens of Periplaneta americana Linnaeus from 13 localities of Ica were studied. Nematodes and protozoa were identified. They are Lophomonas blattarum, Leptomonas sp., Leidynema appendiculatum and Hammerschmidtiella diesingi. Also, parasites of man were found, three of which are pathogenic: Giardia lamblia, Blastocystis hominis and Cryptosporidium sp.

  17. 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 squares...

  18. 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…

  19. 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

  20. Harmonic component detection: Optimized Spectral Kurtosis for operational modal analysis

    Science.gov (United States)

    Dion, J.-L.; Tawfiq, I.; Chevallier, G.

    2012-01-01

    This work is a contribution in the field of Operational Modal Analysis to identify the modal parameters of mechanical structures using only measured responses. The study deals with structural responses coupled with harmonic components amplitude and frequency modulated in a short range, a common combination for mechanical systems with engines and other rotating machines in operation. These harmonic components generate misleading data interpreted erroneously by the classical methods used in OMA. The present work attempts to differentiate maxima in spectra stemming from harmonic components and structural modes. The detection method proposed is based on the so-called Optimized Spectral Kurtosis and compared with others definitions of Spectral Kurtosis described in the literature. After a parametric study of the method, a critical study is performed on numerical simulations and then on an experimental structure in operation in order to assess the method's performance.

  1. Components of Program for Analysis of Spectra and Their Testing

    Directory of Open Access Journals (Sweden)

    Ivan Taufer

    2013-11-01

    Full Text Available The spectral analysis of aqueous solutions of multi-component mixtures is used for identification and distinguishing of individual componentsin the mixture and subsequent determination of protonation constants and absorptivities of differently protonated particles in the solution in steadystate (Meloun and Havel 1985, (Leggett 1985. Apart from that also determined are the distribution diagrams, i.e. concentration proportions ofthe individual components at different pH values. The spectra are measured with various concentrations of the basic components (one or severalpolyvalent weak acids or bases and various pH values within the chosen range of wavelengths. The obtained absorbance response area has to beanalyzed by non-linear regression using specialized algorithms. These algorithms have to meet certain requirements concerning the possibility ofcalculations and the level of outputs. A typical example is the SQUAD(84 program, which was gradually modified and extended, see, e.g., (Melounet al. 1986, (Meloun et al. 2012.

  2. Weighted principal component analysis: a weighted covariance eigendecomposition approach

    CERN Document Server

    Delchambre, Ludovic

    2014-01-01

    We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration. This method allows one to retrieve a given number of orthogonal principal components amongst the most meaningful ones for the case of problems with weighted and/or missing data. Principal coefficients are then retrieved by fitting principal components to the data while providing the final decomposition. Tests performed on real and simulated cases show that our method is optimal in the identification of the most significant patterns within data sets. We illustrate the usefulness of this method by assessing its quality on the extrapolation of Sloan Digital Sky Survey quasar spectra from measured wavelengths to shorter and longer wavelengths. Our new algorithm also benefits from a fast and flexible implementation.

  3. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho

    2010-09-01

    We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

  4. Maximum flow-based resilience analysis: From component to system

    Science.gov (United States)

    Jin, Chong; Li, Ruiying; Kang, Rui

    2017-01-01

    Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel systems using Zobel’s resilience measure. The two analytic models can be used to evaluate quantitatively and compare the resilience of the systems with the corresponding performance structures. For systems with identical components, the resilience of the parallel system increases with increasing number of components, while the resilience remains constant in the series system. A Monte Carlo-based simulation method is also provided to verify the correctness of our analytic resilience models and to analyze the resilience of networked systems based on that of components. A road network example is used to illustrate the analysis process, and the resilience comparison among networks with different topologies but the same components indicates that a system with redundant performance is usually more resilient than one without redundant performance. However, not all redundant capacities of components can improve the system resilience, the effectiveness of the capacity redundancy depends on where the redundant capacity is located. PMID:28545135

  5. Fatigue Reliability Analysis of Wind Turbine Cast Components

    Directory of Open Access Journals (Sweden)

    Hesam Mirzaei Rafsanjani

    2017-04-01

    Full Text Available The fatigue life of wind turbine cast components, such as the main shaft in a drivetrain, is generally determined by defects from the casting process. These defects may reduce the fatigue life and they are generally distributed randomly in components. The foundries, cutting facilities and test facilities can affect the verification of properties by testing. Hence, it is important to have a tool to identify which foundry, cutting and/or test facility produces components which, based on the relevant uncertainties, have the largest expected fatigue life or, alternatively, have the largest reliability to be used for decision-making if additional cost considerations are added. In this paper, a statistical approach is presented based on statistical hypothesis testing and analysis of covariance (ANCOVA which can be applied to compare different groups (manufacturers, suppliers, test facilities, etc. and to quantify the relevant uncertainties using available fatigue tests. Illustrative results are presented as obtained by statistical analysis of a large set of fatigue data for casted test components typically used for wind turbines. Furthermore, the SN curves (fatigue life curves based on applied stress for fatigue assessment are estimated based on the statistical analyses and by introduction of physical, model and statistical uncertainties used for the illustration of reliability assessment.

  6. Polynomial analysis of canopy spectra and biochemical component content inversion

    Institute of Scientific and Technical Information of China (English)

    YAN Chunyan; LIU Qiang; NIU Zheng; WANG Jihua; HUANG Wenjiang; LIU Liangyun

    2005-01-01

    A polynomial expression model was developed in this paper to describe directional canopy spectra, and the decomposition of the polynomial expression was used as a tool for retrieving biochemical component content from canopy multi-angle spectra. First, the basic formula of the polynomial expression was introduced and the physical meaning of its terms and coefficients was discussed. Based on this analysis, a complete polynomial expression model and its decomposition method were given. By decomposing the canopy spectra simulated with SAILH model, it shows that the polynomial expression can not only fit well the canopy spectra, but also show the contribution of every order scattering to the whole reflectance. Taking the first scattering coefficients a10 and a01 for example, the test results show that the polynomial coefficients reflect very well the hot spot phenomenon and the effects of viewing angles, LAI and leaf inclination angle on canopy spectra. By coupling the polynomial expression with leaf model PROSPECT, a canopy biochemical component content inversion model was given. In the simulated test, the canopy multi-angle spectra were simulated by two different models, SAILH and 4-SCALE respectively, then the biochemical component content was retrieved by inverting the coupled polynomial expression + PROSPECT model. Results of the simulated test are promising, and when applying the algorithm to measured corn canopy multi-angle spectra, we also get relatively accurate chlorophyll content. It shows that the polynomial analysis provides a new method to get biochemical component content independent of any specific canopy model.

  7. 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 met

  8. Application of Ica-Eemd to Secure Communications in Chaotic Systems

    Science.gov (United States)

    Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E.

    2012-04-01

    We propose the application of ICA-EEMD to secure communication systems. ICA-EEMD is employed to retrieve the message data encrypted by a mixture of Gaussian white noise and chaotic noise. The results showed that ICA-EEMD can effectively extract the two original message data.

  9. Vascular plug for ICA occlusion in cavernous carotid aneurysms: technical note

    Energy Technology Data Exchange (ETDEWEB)

    Scott, David A.; Keston, Peter; White, Philip; Sellar, Robin [Western General Hospital, Division of Clinical Neurosciences, Edinburgh (United Kingdom)

    2008-09-15

    Large, symptomatic aneurysms of the cavernous internal carotid artery (ICA) can be successfully treated by a combination of aneurysm coiling and occlusion of the parent vessel. We describe the use of an Amplatzer (AGA medical corporation, Plymouth, MA, USA) detachable nitinol vascular plug to occlude the ICA in four patients with symptomatic cavernous ICA aneurysms. (orig.)

  10. Component-based analysis of embedded control applications

    DEFF Research Database (Denmark)

    Angelov, Christo K.; Guan, Wei; Marian, Nicolae

    2011-01-01

    presents an analysis technique that can be used to validate COMDES design models in SIMULINK. It is based on a transformation of the COMDES design model into a SIMULINK analysis model, which preserves the functional and timing behaviour of the application. This technique has been employed to develop...... configuration of applications from validated design models and trusted components. This design philosophy has been instrumental for developing COMDES—a component-based framework for distributed embedded control systems. A COMDES application is conceived as a network of embedded actors that are configured from...... instances of reusable, executable components—function blocks (FBs). System actors operate in accordance with a timed multitasking model of computation, whereby I/O signals are exchanged with the controlled plant at precisely specified time instants, resulting in the elimination of I/O jitter. The paper...

  11. Principal Component Analysis of Thermal Dorsal Hand Vein Pattern Architecture

    Directory of Open Access Journals (Sweden)

    V. Krishna Sree

    2012-12-01

    Full Text Available The quest of providing more secure identification system has lead to rise in developing biometric systems. Biometrics such as face, fingerprint and iris have been developed extensively for human identification purpose and also to provide authentic input to many security systems in the past few decades. Dorsal hand vein pattern is an emerging biometric which is unique to every individual. In this study principal component analysis is used to obtain Eigen vein patterns which are low dimensional representation of vein pattern features. The extraction of the vein patterns was obtained by morphological techniques. Noise reduction filters are used to enhance the vein patterns. Principle component analysis is able to reduce the 2-dimensional image database into 1-dimensional Eigen vectors and able to identify all the dorsal hand pattern images.

  12. QUALITY CONTROL OF SEMICONDUCTOR PACKAGING BASED ON PRINCIPAL COMPONENTS ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    5 critical quality characteristics must be controlled in the surface mount and wire-bond process in semiconductor packaging. And these characteristics are correlated with each other. So the principal components analysis(PCA) is used in the analysis of the sample data firstly. And then the process is controlled with hotelling T2 control chart for the first several principal components which contain sufficient information. Furthermore, a software tool is developed for this kind of problems. And with sample data from a surface mounting device(SMD) process, it is demonstrated that the T2 control chart with PCA gets the same conclusion as without PCA, but the problem is transformed from high-dimensional one to a lower dimensional one, i.e., from 5 to 2 in this demonstration.

  13. Microcalorimeter pulse analysis by means of principle component decomposition

    CERN Document Server

    de Vries, C P; van der Kuur, J; Gottardi, L; Akamatsu, H

    2016-01-01

    The X-ray integral field unit for the Athena mission consists of a microcalorimeter transition edge sensor pixel array. Incoming photons generate pulses which are analyzed in terms of energy, in order to assemble the X-ray spectrum. Usually this is done by means of optimal filtering in either time or frequency domain. In this paper we investigate an alternative method by means of principle component analysis. This method attempts to find the main components of an orthogonal set of functions to describe the data. We show, based on simulations, what the influence of various instrumental effects is on this type of analysis. We compare analyses both in time and frequency domain. Finally we apply these analyses on real data, obtained via frequency domain multiplexing readout.

  14. 基于新概率密度函数的ICA盲源分离%ICA Blind Signal Separation Based on a New Probability Density Function

    Institute of Scientific and Technical Information of China (English)

    张娟娟; 邸双亮

    2014-01-01

    This paper is concerned with the blind source separation (BSS) problem of super-Gaussian and sub-Gaussian mixed signal by using the maximum likelihood method, which is based on independent component analysis (ICA) method. In this paper, we construct a new type of probability density function (PDF) which is different from the already existing PDF used to separate mixed signals in the previously published papers. Applying the new constructed PDF to estimate probability density of super-Gaussian and sub-Gaussian signals (assuming the source signals are independent of each other), it is not necessary to change the parameter values artificially, and the separation work may be performed adaptively. Numerical experiments verify the feasibility of the newly constructed PDF, and the convergence time and the separation effect are improved compared with the original algorithm.%基于独立分量分析(Independent Component Analysis, ICA),利用极大似然估计法,研究了超高斯和亚高斯的混合信号的盲源分离(Blind Sources Separation, BSS)问题。文中构造了一种新的、不同于以往文章中用来分离混合信号的概率密度函数(Probability Density Function, PDF)。新构造的PDF无需改变函数中的参数值,可用来对于超高斯和亚高斯信号的概率密度进行估计(假设未知源信号是相互独立的)。数值实验验证了新构造的PDF的可行性,与原算法相比,收敛时间和分离效果都得到了较大的改善。

  15. A Sensitivity Analysis on Component Reliability from Fatigue Life Computations

    Science.gov (United States)

    1992-02-01

    AD-A247 430 MTL TR 92-5 AD A SENSITIVITY ANALYSIS ON COMPONENT RELIABILITY FROM FATIGUE LIFE COMPUTATIONS DONALD M. NEAL, WILLIAM T. MATTHEWS, MARK G...HAGI OR GHANI NUMBI:H(s) Donald M. Neal, William T. Matthews, Mark G. Vangel, and Trevor Rudalevige 9. PERFORMING ORGANIZATION NAME AND ADDRESS lU...Technical Information Center, Cameron Station, Building 5, 5010 Duke Street, Alexandria, VA 22304-6145 2 ATTN: DTIC-FDAC I MIAC/ CINDAS , Purdue

  16. A Constrained EM Algorithm for Independent Component Analysis

    OpenAIRE

    Welling, Max; Weber, Markus

    2001-01-01

    We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm. The source distributions are modeled as D one-dimensional mixtures of gaussians. The observed data are modeled as linear mixtures of the sources with additive, isotropic noise. This generative model is fit to the data using constrained EM. The simpler “soft-switching” approach is introduced, which uses only one parameter to decide on the sub- or sup...

  17. Primary component analysis method and reduction of seismicity parameters

    Institute of Scientific and Technical Information of China (English)

    WANG Wei; MA Qin-zhong; LIN Ming-zhou; WU Geng-feng; WU Shao-chun

    2005-01-01

    In the paper, the primary component analysis is made using 8 seismicity parameters of earthquake frequency N (ML≥3.0), b-value, 7-value, A(b)-value, Mf-value, Ac-value, C-value and D-value that reflect the characteristics of magnitude, time and space distribution of seismicity from different respects. By using the primary component analysis method, the synthesis parameter W reflecting the anomalous features of earthquake magnitude, time and space distribution can be gained. Generally, there is some relativity among the 8 parameters, but their variations are different in different periods. The earthquake prediction based on these parameters is not very well. However,the synthesis parameter W showed obvious anomalies before 13 earthquakes (MS>5.8) occurred in North China,which indicates that the synthesis parameter W can reflect the anomalous characteristics of magnitude, time and space distribution of seismicity better. Other problems related to the conclusions drawn by the primary component analysis method are also discussed.

  18. Structure analysis of active components of traditional Chinese medicines

    DEFF Research Database (Denmark)

    Zhang, Wei; Sun, Qinglei; Liu, Jianhua

    2013-01-01

    Traditional Chinese Medicines (TCMs) have been widely used for healing of different health problems for thousands of years. They have been used as therapeutic, complementary and alternative medicines. TCMs usually consist of dozens to hundreds of various compounds, which are extracted from raw...... samples. NMR, on the other hand, provides not only information of primary structures but also information of higher order structures, complementing the components structure analysis by HPLC-MS. The most recent progress in the analysis of the commonly used TCMs will be summarized...

  19. Extension of a System Level Tool for Component Level Analysis

    Science.gov (United States)

    Majumdar, Alok; Schallhorn, Paul

    2002-01-01

    This paper presents an extension of a numerical algorithm for network flow analysis code to perform multi-dimensional flow calculation. The one dimensional momentum equation in network flow analysis code has been extended to include momentum transport due to shear stress and transverse component of velocity. Both laminar and turbulent flows are considered. Turbulence is represented by Prandtl's mixing length hypothesis. Three classical examples (Poiseuille flow, Couette flow and shear driven flow in a rectangular cavity) are presented as benchmark for the verification of the numerical scheme.

  20. NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation.However, PCA sometime is unfeasible to nonlinear feature analysis and limited in application to nonlinear industrial process.Kernel PCA (KPCA) is extension of PCA and can be used for nonlinear feature analysis.A nonlinear data reconciliation method based on KPCA is proposed.The basic idea of this method is that firstly original data are mapped to high dimensional feature space by nonlinear function, and PCA is implemented in the feature space.Then nonlinear feature analysis is implemented and data are reconstructed by using the kernel.The data reconciliation method based on KPCA is applied to ternary distillation column.Simulation results show that this method can filter the noise in measurements of nonlinear process and reconciliated data can represent the true information of nonlinear process.

  1. Correlation and principal component analysis in ceramic tiles characterization

    Directory of Open Access Journals (Sweden)

    Podunavac-Kuzmanović Sanja O.

    2015-01-01

    Full Text Available The present study deals with the analysis of the characteristics of ceramic wall and floor tiles on the basis of their quality parameters: breaking force, flexural strenght, absorption and shrinking. Principal component analysis was applied in order to detect potential similarities and dissimilarities among the analyzed tile samples, as well as the firing regimes. Correlation analysis was applied in order to find correlations among the studied quality parameters of the tiles. The obtained results indicate particular differences between the samples on the basis of the firing regimes. However, the correlation analysis points out that there is no statistically significant correlation among the quality parameters of the studied samples of the wall and floor ceramic tiles.[Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. III 45008

  2. Study on failure analysis of array chip components in IRFPA

    Science.gov (United States)

    Zhang, Xiaonan; He, Yingjie; Li, Jinping

    2016-10-01

    Infrared focal plane array detector has advantages of strong anti-interference ability and high sensitivity. Its size, weight and power dissipation has been noticeably decreased compared to the conventional infrared imaging system. With the development of the detector manufacture technology and the cost reduction, IRFPA detector has been widely used in the military and commercial fields. Due to the restricting of array chip manufacturing process and material defects, the fault phenomenon such as cracking, bad pixel and abnormal output was showed during the test, which restricts the performance of the infrared detector imaging system, and these effects are gradually intensified with the expanding of the focal plane array size and the shrinking of the pixel size. Based on the analysis of the test results for the infrared detector array chip components, the fault phenomenon was classified. The main cause of the chip component failure is chip cracking, bad pixel and abnormal output. The reason of the failure has been analyzed deeply. According to analyze the mechanism of the failure, a series of measures which contain filtrating materials and optimizing the manufacturing process of array chip components were used to improve the performance of the chip components and the test pass rate, which is used to meet the needs of the detector performance.

  3. 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

    -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...... for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength....

  4. A comparative study of principal component analysis and independent component analysis in eddy current pulsed thermography data processing

    Science.gov (United States)

    Bai, Libing; Gao, Bin; Tian, Shulin; Cheng, Yuhua; Chen, Yifan; Tian, Gui Yun; Woo, W. L.

    2013-10-01

    Eddy Current Pulsed Thermography (ECPT), an emerging Non-Destructive Testing and Evaluation technique, has been applied for a wide range of materials. The lateral heat diffusion leads to decreasing of temperature contrast between defect and defect-free area. To enhance the flaw contrast, different statistical methods, such as Principal Component Analysis and Independent Component Analysis, have been proposed for thermography image sequences processing in recent years. However, there is lack of direct and detailed independent comparisons in both algorithm implementations. The aim of this article is to compare the two methods and to determine the optimized technique for flaw contrast enhancement in ECPT data. Verification experiments are conducted on artificial and thermal fatigue nature crack detection.

  5. Interaction Analysis of a Two-Component System Using Nanodiscs.

    Directory of Open Access Journals (Sweden)

    Patrick Hörnschemeyer

    Full Text Available Two-component systems are the major means by which bacteria couple adaptation to environmental changes. All utilize a phosphorylation cascade from a histidine kinase to a response regulator, and some also employ an accessory protein. The system-wide signaling fidelity of two-component systems is based on preferential binding between the signaling proteins. However, information on the interaction kinetics between membrane embedded histidine kinase and its partner proteins is lacking. Here, we report the first analysis of the interactions between the full-length membrane-bound histidine kinase CpxA, which was reconstituted in nanodiscs, and its cognate response regulator CpxR and accessory protein CpxP. Using surface plasmon resonance spectroscopy in combination with interaction map analysis, the affinity of membrane-embedded CpxA for CpxR was quantified, and found to increase by tenfold in the presence of ATP, suggesting that a considerable portion of phosphorylated CpxR might be stably associated with CpxA in vivo. Using microscale thermophoresis, the affinity between CpxA in nanodiscs and CpxP was determined to be substantially lower than that between CpxA and CpxR. Taken together, the quantitative interaction data extend our understanding of the signal transduction mechanism used by two-component systems.

  6. Assessment of cluster yield components by image analysis.

    Science.gov (United States)

    Diago, Maria P; Tardaguila, Javier; Aleixos, Nuria; Millan, Borja; Prats-Montalban, Jose M; Cubero, Sergio; Blasco, Jose

    2015-04-01

    Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R(2) between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry.

  7. A Novel Repressor of the ica Locus Discovered in Clinically Isolated Super-Biofilm-Elaborating Staphylococcus aureus

    Science.gov (United States)

    Yu, Liansheng; Hisatsune, Junzo; Hayashi, Ikue; Tatsukawa, Nobuyuki; Sato’o, Yusuke; Mizumachi, Emiri; Kato, Fuminori; Hirakawa, Hideki; Pier, Gerald B.

    2017-01-01

    ABSTRACT Staphylococcus aureus TF2758 is a clinical isolate from an atheroma and a super-biofilm-elaborating/polysaccharide intercellular adhesin (PIA)/poly-N-acetylglucosamine (PNAG)-overproducing strain (L. Shrestha et al., Microbiol Immunol 60:148–159, 2016, https://doi.org/10.1111/1348-0421.12359). A microarray analysis and DNA genome sequencing were performed to identify the mechanism underlying biofilm overproduction by TF2758. We found high transcriptional expression levels of a 7-gene cluster (satf2580 to satf2586) and the ica operon in TF2758. Within the 7-gene cluster, a putative transcriptional regulator gene designated rob had a nonsense mutation that caused the truncation of the protein. The complementation of TF2758 with rob from FK300, an rsbU-repaired derivative of S. aureus strain NCTC8325-4, significantly decreased biofilm elaboration, suggesting a role for rob in this process. The deletion of rob in non-biofilm-producing FK300 significantly increased biofilm elaboration and PIA/PNAG production. In the search for a gene(s) in the 7-gene cluster for biofilm elaboration controlled by rob, we identified open reading frame (ORF) SAOUHSC_2898 (satf2584). Our results suggest that ORF SAOUHSC_2898 (satf2584) and icaADBC are required for enhanced biofilm elaboration and PIA/PNAG production in the rob deletion mutant. Rob bound to a palindromic sequence within its own promoter region. Furthermore, Rob recognized the TATTT motif within the icaR-icaA intergenic region and bound to a 25-bp DNA stretch containing this motif, which is a critically important short sequence regulating biofilm elaboration in S. aureus. Our results strongly suggest that Rob is a long-sought repressor that recognizes and binds to the TATTT motif and is an important regulator of biofilm elaboration through its control of SAOUHSC_2898 (SATF2584) and Ica protein expression in S. aureus. PMID:28143981

  8. Robust artifactual independent component classification for BCI practitioners

    Science.gov (United States)

    Winkler, Irene; Brandl, Stephanie; Horn, Franziska; Waldburger, Eric; Allefeld, Carsten; Tangermann, Michael

    2014-06-01

    Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain-computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.

  9. A principal components analysis of Rorschach aggression and hostility variables.

    Science.gov (United States)

    Katko, Nicholas J; Meyer, Gregory J; Mihura, Joni L; Bombel, George

    2010-11-01

    We examined the structure of 9 Rorschach variables related to hostility and aggression (Aggressive Movement, Morbid, Primary Process Aggression, Secondary Process Aggression, Aggressive Content, Aggressive Past, Strong Hostility, Lesser Hostility) in a sample of medical students (N= 225) from the Johns Hopkins Precursors Study (The Johns Hopkins University, 1999). Principal components analysis revealed 2 dimensions accounting for 58% of the total variance. These dimensions extended previous findings for a 2-component model of Rorschach aggressive imagery that had been identified using just 5 or 6 marker variables (Baity & Hilsenroth, 1999; Liebman, Porcerelli, & Abell, 2005). In light of this evidence, we draw an empirical link between the historical research literature and current studies of Rorschach aggression and hostility that helps organize their findings. We also offer suggestions for condensing the array of aggression-related measures to simplify Rorschach aggression scoring.

  10. Flexibility in Supply Chain. A case study of ICA AB (Non-Food/Clothing) and sub-case of ZARA

    OpenAIRE

    Povarava, Nastassia; Borovkova, Natalija

    2012-01-01

    Problem – The essential problem being analyzed in the research paper is the methods of improving supply chain flexibility under certain circumstances and constrains that are imposed on the company. Purpose - The paper aims at providing suggestions on improvement of supply chain flexibility for ICA AB (Clothing) based on comparative analysis on sub-case study of ZARA. The major part of analysis is based on investigation of the relationship between supply chain characteristics and firm performa...

  11. Multigroup Moderation Test in Generalized Structured Component Analysis

    Directory of Open Access Journals (Sweden)

    Angga Dwi Mulyanto

    2016-05-01

    Full Text Available Generalized Structured Component Analysis (GSCA is an alternative method in structural modeling using alternating least squares. GSCA can be used for the complex analysis including multigroup. GSCA can be run with a free software called GeSCA, but in GeSCA there is no multigroup moderation test to compare the effect between groups. In this research we propose to use the T test in PLS for testing moderation Multigroup on GSCA. T test only requires sample size, estimate path coefficient, and standard error of each group that are already available on the output of GeSCA and the formula is simple so the user does not need a long time for analysis.

  12. Principal components null space analysis for image and video classification.

    Science.gov (United States)

    Vaswani, Namrata; Chellappa, Rama

    2006-07-01

    We present a new classification algorithm, principal component null space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i," an Mi-dimensional subspace along which the class' intraclass variance is the smallest. We call this subspace an approximate null space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class' mean in the class' ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video--an action retrieval problem and abnormal activity detection.

  13. Principal component analysis of FDG PET in amnestic MCI

    Energy Technology Data Exchange (ETDEWEB)

    Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido [University of Genoa, Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, Genoa (Italy); S. Martino Hospital, Alzheimer Evaluation Unit, Genoa (Italy); S. Martino Hospital, Head-Neck Department, Genoa (Italy); Salmaso, Dario [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Morbelli, Silvia [University of Genoa, Nuclear Medicine Unit, Department of Internal Medicine, Genoa (Italy); Piccardo, Arnoldo [Galliera Hospital, Nuclear Medicine Unit, Department of Imaging Diagnostics, Genoa (Italy); Larsson, Stig A. [Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden); Pagani, Marco [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden)

    2008-12-15

    The purpose of the study is to evaluate the combined accuracy of episodic memory performance and {sup 18}F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). {sup 18}F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and {sup 18}F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

  14. Protein structure similarity from principle component correlation analysis

    Directory of Open Access Journals (Sweden)

    Chou James

    2006-01-01

    Full Text Available Abstract Background Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. Results We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. Conclusion The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum

  15. 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

  16. PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

    Directory of Open Access Journals (Sweden)

    Hermita Bus Umar

    2009-03-01

    Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.

  17. Eliminating the Influence of Harmonic Components in Operational Modal Analysis

    DEFF Research Database (Denmark)

    Jacobsen, Niels-Jørgen; Andersen, Palle; Brincker, Rune

    2007-01-01

    on the well-known Enhanced Frequency Domain Decomposition (EFDD) technique for eliminating these harmonic components in the modal parameter extraction process. For assessing the quality of the method, various experiments were carried out where the results were compared with those obtained with pure stochastic......Operational modal analysis is used for determining the modal parameters of structures for which the input forces cannot be measured. However, the algorithms used assume that the input forces are stochastic in nature. While this is often the case for civil engineering structures, mechanical...

  18. Independent Component Analysis to Detect Clustered Microcalcification Breast Cancers

    Directory of Open Access Journals (Sweden)

    R. Gallardo-Caballero

    2012-01-01

    current reproducible studies on the same mammogram set. This proposal is mainly based on the use of extracted image features obtained by independent component analysis, but we also study the inclusion of the patient’s age as a nonimage feature which requires no human expertise. Our system achieves an average of 2.55 false positives per image at a sensitivity of 81.8% and 4.45 at a sensitivity of 91.8% in diagnosing the BCRP_CALC_1 subset of DDSM.

  19. Nonlinear Principal Component Analysis Using Strong Tracking Filter

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

  20. Nuclear analysis techniques as a component of thermoluminescence dating

    Energy Technology Data Exchange (ETDEWEB)

    Prescott, J.R.; Hutton, J.T.; Habermehl, M.A. [Adelaide Univ., SA (Australia); Van Moort, J. [Tasmania Univ., Sandy Bay, TAS (Australia)

    1996-12-31

    In luminescence dating, an age is found by first measuring dose accumulated since the event being dated, then dividing by the annual dose rate. Analyses of minor and trace elements performed by nuclear techniques have long formed an essential component of dating. Results from some Australian sites are reported to illustrate the application of nuclear techniques of analysis in this context. In particular, a variety of methods for finding dose rates are compared, an example of a site where radioactive disequilibrium is significant and a brief summary is given of a problem which was not resolved by nuclear techniques. 5 refs., 2 tabs.