Blind channel deconvolution of real world signals using source separation techniques

In this paper we present a method for blind deconvolution of linear channels based on source separation techniques, for real word signals. This technique applied to blind deconvolution problems is based in exploiting not the spatial independence between signals but the temporal independence between samples of the signal. Our objective is to minimize the mutual information between samples of the output in order to retrieve the original signal. In order to make use of use this idea the input signal must be a non-Gaussian i.i.d. signal. Because most real world signals do not have this i.i.d. nature, we will need to preprocess the original signal before the transmission into the channel. Likewise we should assure that the transmitted signal has non-Gaussian statistics in order to achieve the correct function of the algorithm. The strategy used for this preprocessing will be presented in this paper. If the receiver has the inverse of the preprocess, the original signal can be reconstructed without the convolutive distortion.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Blind Inversion of Wiener Systems

A system in which a linear dynamic part is followed by a non linear memoryless distortion a Wiener system is blindly inverted This kind of systems can be modelised as a postnonlinear mixture and using some results about these mixtures an e cient algorithm is proposed Results in a hard situation are presented and illustrate the e ciency of this algorithm

Differences of Functional Connectivity Brain Network in Emotional Judgment

Using combined emotional stimuli, combining photos of faces and recording of voices, we investigated the neural dynamics of emotional judgment using scalp EEG recordings. Stimuli could be either combioned in a congruent, or a non-congruent way.. As many evidences show the major role of alpha in emotional processing, the alpha band was subjected to be analyzed. Analysis was performed by computing the synchronization of the EEGs and the conditions congruent vs. non-congruent were compared using statistical tools. The obtained results demonstrate that scalp EEG ccould be used as a tool to investigate the neural dynamics of emotional valence and discriminate various emotions (angry, happy and neutral stimuli).

Parameterization of written signatures based on EFD

In this work we propose a method to quantify written signatures from digitalized images based on the use of Elliptical Fourier Descriptors (EFD). As usually signatures are not represented as a closed contour, and being that a necessary condition in order to apply EFD, we have developed a method that represents the signatures by means of a set of closed contours. One of the advantages of this method is that it can reconstruct the original shape from all the coefficients, or an approximated shape from a reduced set of them finding the appropriate number of EFD coefficients required for preserving the important information in each application. EFD provides accurate frequency information, thus the use of EFD opens many possibilities. The method can be extended to represent other kind of shapes.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

La gestión de los libros electrónicos en la Biblioteca Virtual de la UOC

El texto que se presenta muestra cómo se lleva a cabo la gestión de los libros electrónicos en la Biblioteca Virtual (en adelante BV) de la Universitat Oberta de Catalunya (en adelante UOC). La BV pone especial énfasis en la adquisición de libros digitales para mejorar el acceso de los usuarios a los recursos y a lascolecciones de una universidad caracterizada por su virtualidad. El documento presenta, en primer lugar, el entorno en el que se adquieren y se utilizan los libros electrónicos: se describen los distintos escenarios de adquisición en los que se puede encontrar la BV y se definen los circuitos internos que permiten su gestión, así como los procesos técnicos de los documentos. A continuación, se muestran las distintas opciones de acceso y consulta de libros electrónicos que actualmente se ofrecen desde la BV y se exponenlos análisis de uso de dichos documentos. Por último, se presentan las conclusiones a las que llega la BV sobre el nuevo contexto de libros electrónicos.

Sistema de community cloud aplicat a la preservació digital personal (personal archiving) PYRAMID

La preservació digital (PD) s'ha convertit en un problema persistent per a tots els que vulguin conservar la seva informació digital, garantir el seu estat i consultar aquest informació en el transcurs del temps. Fins ara només grans institucions amb coneixement expert i eines especialitzades han pogut fer front a aquest problema, però la preservació digital no pot ser abordada per una sola institució o nació. Les biblioteques, arxius i altres institucions de conservació de la memòria comparteixen aquest repte de la mateixa manera que els col•leccionistes i creadors, que ho fan a títol individual.L’objectiu del projecte és crear l'aplicació Pyramid que està concebuda com una eina de suport orientada a l'usuari domèstic (sense coneixements tècnics ni de preservació) per a la preservació a mig i llarg termini de col•leccions digitals, texts i vídeos, tal que funcioni com un antivirus (en BackGround) i preservi la informació sense requerir un cost addicional a l'ordinador i que l'usuari no noti cap molèstia a l'hora de fer les seves tasques diàries

La autoreferencialidad en el arte. El metalenguaje en el medio digital. L'autoreferencialitat en l'art. El metallenguatge en el mitjà digital.

Este artículo analiza las obras de algunos artistas digitales que utilizan los recursos propios del medio digital como mensaje central. Nuestra intención será exponer las múltiples formas de utilizar, desarrollar y manipular el metalenguaje. Primeramente, con la intención de comprender estas obras, se elabora un contexto en que se plantearán unos precedentes, para ver, en una segunda parte, cómo el metalenguaje en nuestra actualidad artística y digital vuelve a ser muy utilizado, de manera que llega a convertirse en una metodología característica del arte contemporáneo y de los medios digitales. Palabras clave aleatoriedad, simulación, glitch, net art, interfaz, reactividad, interactividad

La autoreferencialidad en el arte. El metalenguaje en el medio digital

Este artículo analiza las obras de algunos artistas digitales que utilizan los recursos propios del medio digital como mensaje central. Nuestra intención será exponer las múltiples formas de utilizar, desarrollar y manipular el metalenguaje. Primeramente, con la intención de comprender estas obras, se elabora un contexto en que se plantearán unos precedentes, para ver, en una segunda parte, cómo el metalenguaje en nuestra actualidad artística y digital vuelve a ser muy utilizado, de manera que llega a convertirse en una metodología característica del arte contemporáneo y de los medios digitales. Palabras clave aleatoriedad, simulación, glitch, net art, interfaz, reactividad, interactividad