Ica

In the context of Face Recognition the paper compares between Principal Component Analyses (PCA) and Independent Component Analysis (ICA). In the psychological and algorithmic literature Principal Component Analyses (PCA) and Independent Component Analyses (ICA) are the basis of numerous studies. Classical technique in statistical data analyses is called Principal Component Analyses (PCA) and technique of array processing and data analysis is called Independent Component Analysis (ICA). This paper gives the better concept how each algorithms are worked. This concept helps to advance level experiment. Besides, those method is so strong others system and algorithm. In both case, ICA performs good but not as good as PCA. For Face Recognition Principal Component Analyses basis algorithm represents intelligent suction of a random search within a short time and its can detect to solve the problem.

Cinco regiones, ocho variedades autorizadas, un mundo propio y paralelo donde a ratos parece que el tiempo se detuvo en el medioevo, para luego reencontrarse con una vigorosa modernidad, esa que muestra al resto del mundo como “producto bandera” de un país cada vez más orgulloso de lo que cocina y destila. Conocimos parte de una forma de beber –y comer- digna de vivirse como una aventura con deslices, aciertos, pero siempre con la identidad propia por delante.

A grid computing environment provides a type of distributed computation that is unique because it is not centrally managed and it has the capability to connect heterogeneous resources. A grid system provides location-independent access to the resources and services of geographically distributed machines. An essential ingredient for supporting location-independent computations is the ability to discover resources that have been requested by the users. Because the number of grid users can increase and the grid environment is continuously changing, a scheduler that can discover decentralized resources is needed. Grid resource scheduling is considered to be a complicated, NP-hard problem because of the distribution of resources, the changing conditions of resources, and the unreliability of infrastructure communication. Various artificial intelligence algorithms have been proposed for scheduling tasks in a computational grid. This paper uses the imperialist competition algorithm (ICA) to address the problem of independent task scheduling in a grid environment, with the aim of reducing the makespan. Experimental results compare ICA with other algorithms and illustrate that ICA finds a shorter makespan relative to the others.  Moreover, it converges quickly, finding its optimum solution in less time than the other algorithms.

Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation (HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database image with test image final features to compute performance parameters. It is observed that the performance of the proposed method is better compared to existing methods.

Las plantas nativas de Ica en la actualidad se encuentran en estado crítico debido a la deforestación, la expansión urbana y agrícola, encontrándose hoy en día en pocas zonas. El objetivo del presente estudio fue estudiar la interrelación planta nativa y avifauna en la quebrada de Huarangal, ubicada en el Distrito de Yauca del rosario, Provincia de Ica, localizados en las coordenadas 14°12'27.85"S - 75°27'44.29"O, a una altitud que va desde los 750 a 1050 msnm, durante la estación seca abril a noviembre 2010. Se registro la flora nativa usada por las aves, ya sea como alimento, refugio, percha o lugares de anidamiento. Se realizaron censos mensuales y observaciones directas, utilizando el método de transecto en línea, registrando todas las especies de aves y características del hábitat. Los censos se realizaron desde las 6:00 am hasta 11:00 am. Se registro 29 especies de plantas nativas dentro de 16 familias, donde la más ricas fueron: Cactaceae y Solanaceae (5 Especies c/u) .El tipo de hábito predominante fue el arbusto con 42% del total. Prosopis sp. (Huarango) fue la especie con mayor uso por la avifauna. El número de aves que utilizo la flora fue 23 especies, siendo mayormente usado como alimentación con 63 % del total .Rhodopis vesper y Mimus longicaudatus utilizaron mayor cantidad de plantas con 17 y 10 especies respectivamente. Las aves son muy útiles aportando información sobre el estado de los ecosistemas llevando a cabo las funciones vitales como la polinización, dispersión de semillas y control biológico.

Palabras clave: Plantas nativas, Huarango, Rhodopis vesper, Quebrada Huarangal, Ica

Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5 Hz), alpha-low (6–9 Hz), alpha-high (10–11 Hz), beta (12–19 Hz), and gamma (N21 Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)- based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time–frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains. KEY WORDS: principal components analysis (PCA); exploratory factor analysis (EFA); blind source separation (BSS); resting neural activity; resting EEG; frontal alpha asymmetry; frontal EEG asymmetry.

Independent Component Analysis (ICA) has been successfully used to identify brain related signals and artifacts from multi-channel electroencephalographic (EEG) data. However the stability of ICA decompositions across sessions from a single subject has not been investigated. The goal of this study was to isolate EEG independent components (ICs) across sessions for each subject so as to assess whether ICs are reproducible across sessions. We used 64-channel EEG data recorded from two subjects during a simple mind-wandering experiment. Each subject participated in 11 twenty-minute sessions over a period of five weeks. Extended Infomax ICA decomposition was performed on the continuous data of each session. We used a simple IC clustering technique based on correlation of scalp topographies. Several clusters of homogenous components were identified for each subject. Typical component clusters accounting for eye movement and eye blink artifacts were identified. Both clusters included one component from each recording session. In addition, several clusters corresponding to brain electrical sources, among them clusters exhibiting prominent alpha, beta and Mu band activities, included components from most sessions. These results present evidence that ICA can provide relatively stable solutions across sessions, with important implications for Brain Computer Interface research.

This paper discusses six most frequent methods used to extract different features in Electrocardiograph (ECG) signals namely Autoregressive (AR), Wavelet Transform (WT), Eigenvector, Fast Fourier Transform (FFT), Linear Prediction (LP), and Independent Component Analysis (ICA). The study reveals that Eigenvector method gives better performance in frequency domain for the ECG feature extraction. © 2012 IEEE.

ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6169708

e Comité italien du Bouclier bleu
Le Comité italien du Bouclier bleu est une structure de coordination entre les représentants des ONG du patrimoine (ICOM, ICOMOS, CIA et IFLA), les associations, les institutions et les différents corps culturels nationaux afin de promouvoir le respect et la sauvegarde du patrimoine culturel en Italie, en collaboration avec l’UNESCO et l’ICCROM. Il agit dans le même cadre que le Comité international du Bouclier bleu (ICBS) au niveau international, mais manque actuellement de ressources économiques. Les efforts doivent  tendre entre autres, vers une coordination d’activités au niveau régional,
national et international, mais aussi encourager les partenariats avec des institutions intéressées (écoles, universités, autorités, etc.) et favoriser la formation d’experts aux problèmes de prévention et de sécurité dans le but de
protéger le patrimoine des catastrophes naturelles et des conflits armés.

For the blind separation of sources (BSS) problem (or the independent component analysis (ICA)), it has been shown in many situations, that the adaptive subspace algorithms are very slow and need an important computation e orts. In a previous publication, we proposed a modi ed subspace algorithm for stationary signals. But that algorithm was limited to stationary signals and its convergence was not fast enough. Here, we propose a batch subspace algorithm. The experimental study proves that this algorithm is very fast but its performance are not enough to completely achieve the separation of the independent component of the signals. In the other hand, this algorithm can be used as a pre-processing algorithm to initialized other adaptive subspace algorithms.

In the context of Face Recognition the paper compares between Principal Component Analyses (PCA) and Independent Component Analysis (ICA). In the psychological and algorithmic literature Principal Component Analyses (PCA) and Independent Component Analyses (ICA) are the basis of numerous studies. Classical technique in statistical data analyses is called Principal Component Analyses (PCA) and technique of array processing and data analysis is called Independent Component Analysis (ICA). This paper gives the better concept how each algorithms are worked. This concept helps to advance level experiment. Besides, those method is so strong others system and algorithm. In both case, ICA performs good but not as good as PCA. For Face Recognition Principal Component Analyses basis algorithm represents intelligent suction of a random search within a short time and its can detect to solve the problem.

Brain Computer Interfaces is a communication system between human brain and machines or devices without physical contact by using EEG signals from brain activity. Processing techniques of the EEG signals is very important for the BCI system. This paper presents the algorithm for removing noise from the EEG signal. Filtering of EEG signals requires a filter which automatically adapts to changing input and noise. Adaptive filtering reduces the noise using LMS algorithm. NLMS filter, RLS filter, PCA and ICA methods are compared and evaluated among which PCA gives good result

In this paper we present a novel method for blindly separating unobservable independent component signals from their linear mixtures, using genetic algorithms (GA) to minimize the nonconvex and nonlinear cost functions. This approach is very useful in many fields such as forecasting indexes in financial stock markets where the search for independent components is the major task to include exogenous information into the learning machine. The GA presented in this work is able to extract independent components with faster rate than the previous independent component analysis algorithms based on Higher Order Statistics (HOS) as input space dimension increases showing significant accuracy and robustness.

Research on video games has yielded consistent findings that violent video games increase aggression and decrease prosocial behavior. However, these studies typically examined single-player games. Of interest is the effect of cooperative play in a violent video game on subsequent cooperative or competitive behavior. Participants played Halo II (a first-person shooter game) cooperatively or competitively and then completed a modified prisoner's dilemma task to assess competitive and cooperative behavior. Compared with the competitive play conditions, players in the cooperative condition engaged in more tit-for-tat behaviors-a pattern of behavior that typically precedes cooperative behavior. The social context of game play influenced subsequent behavior more than the content of the game that was played.

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 1H 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.

Independent component analysis (ICA) is a potentially powerful tool for analyzing event-related potentials (ERPs), one of the most popular measures of brain function in cognitive neuroscience. Based on the statistics of the electroencephalogram (EEG), from which
ERPs are derived, ICA may be able to extract multiple, functionally distinct sources of an ERP generated by disparate regions of cerebral cortex. Extracting such sources greatly increases the informativeness of ERPs by providing a cleaner, less ambiguous measure of source activity and by facilitating the identification of this activity across different experimental paradigms. The main purpose of this review article is to explain the logic of ICA, to illustrate how ICA could in principle extract spatiotemporally overlapping ERP sources, and to review evidence that ICA is a well motivated methodology that can extract latent ERP sources in practice. In addition, we close the article by noting potential problems with ICA and by comparing it to three alternative methods for extracting ERP sources/components: spatial principal component analysis, source localization, and temporal principal component analysis.

Despite growing interest in emotion regulation, the degree to which psychophysiological measures of emotion regulation are stable over time remains unknown. We examined four-week test-retest reliability of corrugator electromyographic and eyeblink startle measures of negative emotion and its regulation. Both measures demonstrated similar sensitivity to the emotion manipulation, but only individual differences in corrugator modulation and regulation showed adequate reliability. Startle demonstrated diminished sensitivity to the regulation instructions across assessments and poor reliability. This suggests that corrugator represents a trait-like measure of voluntary emotion regulation, whereas startle should be used with caution for assessing individual differences. The data also suggest that corrugator and startle might index partially dissociable constructs and underscore the need to collectmultiplemeasures of emotion. Descriptors: Emotion regulation, Test-retest reliability, Eyeblink startle, Corrugator EMG

Seizure detection plays a central role in most aspects of epilepsy care. Understanding the complex epileptic signals system is a typical problem in electroencephalographic (EEG) signal processing. This problem requires different analysis to reveal the underlying behavior of EEG signals. An example of this is the non-linear dynamic: mathematical tools applied to biomedical problems with the purpose of extracting features or quantifying EEG data. In this work, we studied epileptic seizure detection independently in each brain rhythms from a multilevel 1D wavelet decomposition followed by the independent component analysis (ICA) representation of multivariate EEG signals. Next, the largest Lyapunov exponents (LLE) and their scaling given by its ± standard deviation are estimated in order to obtain the vectors to be used during the training and classification stage. With this information, a logistic regression classification is proposed with the aim of discriminating between seizure and...