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Alzheimer’s disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Westerncountries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by usingautomatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selectedis based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work isfeature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. Thefeature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speechthat are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful whentraining data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters inthe feature vector in order to enhance the performance of the original system while controlling the computational cost.© 2014 Elsevier Ltd. All rights reserved.

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Ligths is the confluence born of combining the worlds of electronics and computer science to create a solution that fits right into the paradigm of the Internet of Things. This project, defined as a platform and culminated as a product, defines a generic solution for devices to interact with each other via a handmade solution. The product in this case represents a ligth that receives commands via different layers of communication (web sockets, HTTP or Bluetooh) coming from an agnostic client (in this cas an iOs app). After the platform (2 servers, one runnig in the cloud and one running in a Raspberry Pi) compute the first command, they will let the end product know the task that should perform. This project contains different explanations of the solutions as well as some technical detail into the technologies used, creating a discussion on why and which are their benefits among others.

This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and identification within dependent environments that merges with other types of technologies. In this kind of environments, vision algorithms are very costly and require a lot of time to produce a response, which is highly inconvenient since many applications can require action to be taken in real time. A multi-agent system (MAS) can automate the process of analyzing images obtained by cameras, and optimize the procedure. This study presents a MAS that can process stereoscopic images to detect and classify people by combining a series of novel techniques. The article shows in detail the combination of techniques used to perform the detection process. The process can be subdivided into human detection, human tracking, and human behavior understanding. With the addition of a casebased reasoning (CBR) model, the system can also incorporate reasoning capabilities. The system was tested under different conditions and environments.

Objective: Genomic profiling, the use of genetic variants at multiple loci simultaneously for the prediction of disease risk, requires the selection of a set of genetic variants that best predicts disease status. The goal of this work was to provide a new selection algorithm for genomic profiling. Methods: We propose a new algorithm for genomic profiling based on optimizing the area under the receiver operating characteristic curve (AUC) of the random forest (RF). The proposed strategy implements a backward elimination process based on the initial ranking of variables. Results and Conclusions: We demonstrate the advantage of using the AUC instead of the classification error as a measure of predictive accuracy of RF. In particular, we show that the use of the classification error is especially inappropriate when dealing with unbalanced data sets. The new procedure for variable selection and prediction, namely AUC-RF, is illustrated with data from a bladder cancer study and also with simulated data. The algorithm is publicly available as an R package, named AUCRF, at http://cran.r-project.org/. This work was partially supported by grant MTM2008-06747-C02-02 from the Ministerio de Educacion y Ciencia (Spain), grant 050831 from the Marato de TV3 Foundation, grant 2009SGR-581 from the AGAUR-Generalitat de Catalunya and the LMU-innovativ Project BioMed-S

This paper deals with non-linear transformations for improving the performance of an entropy-based voice activity detector (VAD). The idea to use a non-linear transformation has already been applied in the field of speech linear prediction, or linear predictive coding (LPC), based on source separation techniques, where a score function is added to classical equations in order to take into account the true distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if the signal is clean, the estimated entropy is essentially the same; if the signal is noisy, however, the frames transformed using the score function may give entropy that is different in voiced frames as compared to nonvoiced ones. Experimental evidence is given to show that this fact enables voice activity detection under high noise, where the simple entropy method fails.

Interval censoring is encountered in many practical situations when the event of interest cannot be observed and it is only known to have occurred within a time window. The theory for the analysis of interval-censored data has been developed over the past three decades and several reviews have been written. However, it is still a common practice in medical and reliability studies to simplify the interval censoring structure of the data into a more standard right censoring situation by, for instance, imputing the midpoint of the censoring interval. The availability of software for right censoring might well be the main reason for this simplifying practice. In contrast, several methods have been developed to deal with interval-censored data and the corresponding algorithms to make the procedures feasible are scattered across the statistical software or remain behind the personal computers of many researchers. The purpose of this tutorial is to present, in a pedagogical and unified manner, the methodology and the available software for analyzing interval-censored data. The paper covers frequentist non-parametric, parametric and semiparametric estimating approaches, non-parametric tests for comparing survival curves and a section on simulation of interval-censored data. The methods and the software are described using the data from a dental study.

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time–frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts—with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning ofEEGdata are then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al (2007 Neuroimage 34 1443–9).

Ligths is the confluence born of combining the worlds of electronics and computer science to create a solution that fits right into the paradigm of the Internet of Things. This project, defined as a platform and culminated as a product, defines a generic solution for devices to interact with each other via a handmade solution. The product in this case represents a ligth that receives commands via different layers of communication (web sockets, HTTP or Bluetooh) coming from an agnostic client (in this cas an iOs app). After the platform (2 servers, one runnig in the cloud and one running in a Raspberry Pi) compute the first command, they will let the end product know the task that should perform. This project contains different explanations of the solutions as well as some technical detail into the technologies used, creating a discussion on why and which are their benefits among others.

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG). However, choosing suitable measures is a challenging task. Among other measures, frequency relative power (RP) and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency RP on EEG signals, examining the changes found in different frequency ranges. Approach .W efirst explore the use of a single feature for computing the classification rate (CR), looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing mild cognitive impairment (MCI) and healthy subjects (patients age: 71.9±10.2, healthy subjects age: 71.7±8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6±10.0; healthy subjects age: 69.4±11.5). Main results. Using a single feature to compute CRs we achieve a performance of 78.33% for the MCI data set and of 97.56% for Mild AD. Results are clearly improved using the multiple feature classification, where a CR of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using four features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics. Our brains are buzzing with electrical activity moving in and out of neural cells, sending electrical impulses along their axons, and exchanging chemical messages. Neural signals allow us to observe neuronal activity in real time. This special issue aims to cover some problems related to neural signal processing. The origin of this volume is in the Special Sessions on Challenges in Neuroengineering (SSCN) within the International Conference on Neural Computation Theory and Applications (NCTA). A selected choice of papers based on the presentations delivered at SSCN has given rise to this issue of Cognitive Computation. The papers hereinafter deal with the following topics

In this paper the authors propose a new closed contour descriptor that could be seen as a Feature Extractor of closed contours based on the Discrete Hartley Transform (DHT), its main characteristic is that uses only half of the coefficients required by Elliptical Fourier Descriptors (EFD) to obtain a contour approximation with similar error measure. The proposed closed contour descriptor provides an excellent capability of information compression useful for a great number of AI applications. Moreover it can provide scale, position and rotation invariance, and last but not least it has the advantage that both the parameterization and the reconstructed shape from the compressed set can be computed very efficiently by the fast Discrete Hartley Transform (DHT) algorithm. This Feature Extractor could be useful when the application claims for reversible features and when the user needs and easy measure of the quality for a given level of compression, scalable from low to very high quality.

The analysis of fMRI allows mapping the brain and identifying brain regions activated by a particular task. Prior to the analysis, several steps are carried out to prepare the data. One of these is the spatial smoothing whose aim is to eliminate the noise which can cause errors in the analysis. The most common method to perform this is by using a Gaussian filter, in which the extent of smoothing is assumed to be equal across the image. As a result some regions may be under-smoothed, while others may be over-smoothed. Thus, we suggest smoothing the images adaptively using a Wiener filter which allows varying the extent of smoothing according to the changing characteristics of the image. Therefore, we compared the effects of the smoothing with a wiener filter and with a Gaussian Kernel. In general, the results obtained with the adaptive filter were better than those obtained with the Gaussian filter.

We present a new approach to the calculation of solvent-accessible surface areas of molecules with potential application to surface area based methods for determination of solvation free energies. As in traditional analytical and statistical approaches, this new algorithm, called TRIFORCE, reports both component areas and derivatives as a function of the atomic coordinates and radii. Unique to TRIFORCE are the rapid and scalable approaches for the determination of sphere intersection points and numerical estimation of the surface areas, derivatives, and other properties that can be associated with the surface area facets. The algorithm performs a special tessellation and semianalytical integration that uses a precomputed look-up table. This provides a simple way to balance numerical accuracy and memory usage. TRIFORCE calculates derivatives in the same manner, enabling application in force-dependent activities such as molecular geometry minimization. TRIFORCE is available free of charge for academic purposes as both a C++ library, which can be directly interfaced to existing molecular simulation packages, and a web-accessible application.

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 through a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to the improvement 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. The developed system obtains hopeful results for early diagnosis.

This paper deals with the potential and limitations of using voice and speech processing to detect Obstructive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients who present various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set of features for detecting OSA. We apply various feature selection and reduction schemes (statistical ranking, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, Support Vector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects shows that in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able to discriminate quite well between the presence and absence of OSA. However, this is not the case with mild OSA and healthy snoring patients where voice seems to play a secondary role. We found that the best classification schemes are achieved using a Genetic Algorithm for feature selection/reduction.

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients. his work has been partially supported by a SAIOTEK from the Basque Government, University of Vic under the research grant R0904, and the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03. Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country & IKERBASQUE, Basque Foundation for Science

Monitoring the breathing is required in many applications of medical and health fields, but it can be used also in new game applications, for example. In this work, an automatic system for monitoring the breathing is presented. The system uses the acoustic signal recorded by a standard microphone placed in the area of the nostrils. The system is based on a low-complex signal parameterization performed on non-overlapped frames. From this parameterization, a reduced set of real parameters is obtained frame-to-frame. These parameters feed a classifier that performs a classification in three stages: inspiration, transition or retention and expiration providing a fine monitoring of the respiration process. As all of those algorithms are of low complexity and the auxiliary equipment required could only be a standard microphone from a conventional Bluetooth Headset, the system could be able to run in a smartphone device. © 2013 Springer-Verlag Berlin Heidelberg.

When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation) complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum-mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsically complexity the global algorithm is much more slow and hence not useful for our purpose. Fil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Conicet - la Plata. Instituto Argentino de Radioastronomia (i); Argentina Fil: Sole-Casals, Jordi. Universidad de Vic; España

Alzheimer's disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Spontaneous Speech. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis. Nowadays our feature set offers some hopeful conclusions but fails to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is scarce. In this work, the Fractal Dimension (FD) of the observed time series is combined with lineal parameters in the feature vector in order to enhance the performance of the original system.

Speech signals in real scenario ambient are usually mixed with some other signals, such as noise. This may interfere with posterior signal processing applied to the signals. In this work, a new technique of data denoising is presented using multivariate Empirical Mode Decomposition. Different SNR ratios are tested in order to study the evolution of the improvement of the recovered data. An improvement of the analyzed data is obtained with all the SNR levels tested.

When dealing with nonlinear blind deconvolution, complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing or in microarray data analysis. In this paper we propose a simple method to reduce computational time for the inversion of Wiener systems by using a linear approximation in a minimum-mutual information algorithm. Experimental results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. Fil: Sole Casals, Jordi. Universidad de Vic; España Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina

Monitoring and tracking of elderly people using vision algorithms is an strategy gaining relevance to detect anomalous and potentially dangerous situations and react immediately. In general vision algorithms for monitoring and tracking are very costly and take a lot of time to respond, which is highly inconvenient since many applications can require action to be taken in real time. A multi-agent system (MAS) can establish a social model to automate the tasks carried out by the human experts during the process of analyzing images obtained by cameras. This study presents a detector agent integrated in a MAS that can process stereoscopic images to detect and classify situations and states of elderly people in geriatric residences by combining a series of novel techniques. We will talk in details about the combination of techniques used to perform the detection process, subdivided into human detection, human tracking ,and human behavior understanding, and where there is a case-based reasoning (CBR) model that allows the system to add reasoning capabilities.

Wavelet derived codification techniques are widespread used in image codifiers. The wavelet based compression methods are adequate for representing transients. In this paper we explore the use of the discrete wavelet transform analysis of biological signals in order to improve the data compression capability of data coders. The wavelet analysis provides a subband decomposition of any signal, and this enables a lossless or a lossy implementation with the same architecture. The signals could range from speech to sounds or music, but the approach is more orientated to other biosignals like medical signals EEG, ECG or discrete series. Experimental results based on wavelet coefficients quantification, show a lossless compression of 2:1 in all kind of signals, with a fidelity, measured using PSNR, from 79dB to 100dB, and lossy results preserving most of the signal waveform, with a compression ratio from 3:1 to 5:1, with a fidelity from 25dB to 35 dB.

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing in where it has found a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to onedimensional context.

In this paper we use a nonlinear processing technique based on mathematical morphology to develop a simple day trading system that automatically decides the timing to commute the marked strategy in terms of sort/long positions. In this short paper we show preliminary results.

This special issue aims to cover some problems related to non-linear and non-conventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation. The papers hereinafter deal with the following topics: What would happen if we were able to overcome the limitations of audiovisual human systems? The answer could be found in the paper by V. Espinosa-Duro, M. Faundez-Zanuy, and J. Mekyska. A growing recurrent self-organizing model for phoneme recognition is presented by Ch. Jlassi, N. Arous, and N. Ellouze. An algorithm for voiced/unvoiced decision and pitch estimation from speech signals based on classification of peaks using the auto-correlation of the speech multi-scale product is presented by M. A. Ben Massoud, A. Bouzid, and N. Elloze. A feature reduction system based on the Discriminative Common Vector is proposed by C.M. Travieso, M. del Pozo, M.A. Ferrer, and J. B. Alonso. A new approach to the diagnosis of Ataxia SCA-2 by application of independent component analysis to a set of data obtained by electro-oculography is presented by R. V. Garcia, F. Rojas, C. G. Puntonet, B. San Roman, L. Velazquez, and R. Rodriguez. A formant tracking technique based on Fourier ridges detection aiming at improving the performance of these algorithms is presented by I. Jemaa, K. Ouni and Y. Laprie A new architecture for vocabulary independent keyword detection for cognitive virtual agents (SEMAINE system) is proposed by M. Wollmer, F. Eyber, A. Graves, B. Schuller, and G. Rigoll. Their word spotting model is composed of a dynamic-Bayesian network and a bidirectional long–short-term memory recurrent neural network. An insight into non-linear transformations for improving the performance of an entropy-based voice activity detector is made in the paper by two guest editors (J. S. and V.Z.). By no means has the range of topics in this Special Issue covered all directions in non-linear speech processing. We would rather consider it a sample presenting several areas in progress. In any case, we hope that they will be of interest for the readers of Cognitive Computation. Our thanks are due to the Editor-in-Chief, Dr. Amir Hussain, for permanent support in preparation of this issue. Of course, this issue would have been impossible without the contributions of all authors. On the other hand, anonymous referees have also done a great job by carefully J. Sole-Casals (&) V. Zaiats Department of Digital Technologies and Information, Escola Politecnica Superior, Universitat de Vic, c/. de la Laura, 13, 08500 Vic (Barcelona), Spain e-mail: jordi.sole@uvic.cat

In previous work UAS have been proposed as the platform of choice for performing Navaid Flight Inspections. It is expected that the use of these platforms will significantly reduce time and monetary costs, but a number of issues must be addressed for UAS to become a real alternative. An important requirement for flight inspection missions is to minimize their impact on other air traffic. In this paper we focus on provisioning UAS with a level of operational agility comparable to that provided by a human on-board pilot. To avoid conflicts, the unmanned vehicle must be able to momentarily interrupt and later on resume the inspection operations in an automated manner. In this paper we analyze the operational requirements for a specific inspection mission and design a flight plan for its execution. Afterwards, a flight plan specification formalism, that will enable us to perform the inspection mission in a highly automated manner, is presented. Finally, we demonstrate how the flight plan design is translated to a suitable representation using this formalism and how the required operational agility is achieved.

The main objective of this paper is to contribute to the inherent necessity of the aerialtransport system to guarantee the safety of its users in an efficient manner. This efficiency isrequired to accomplish the objective of transport people and goods with cost content enoughto allow the access to this system at the biggest number of users. To improve this efficiencywe propose a new paradigm in Flight Inspection Systems. This new paradigm separates theflight inspection platform in two separate segments, remaining in the air segment the strictlyneeded resources, the sensors, by mean of Unmanned Aerial Systems (UAS).

Bump modeling is a method used to extract oscillatory bursts in electrophysiological signals, who are most likely to be representative of local synchronies. In this paper we present an improved sparse bump modeling method. The improvements are done in the adaptation method by optimizing the parameters according to the order of their derivatives; and in the window matching method by changing the selection of the initial function. Experimental results, comparing previous method vs the improved version, show that the obtained model fits better the signal, hence the result will be much more precise and useful.

T]his work introduces a flexible and reusable architecture designed to facilitate the development of remote sensing applications. Based on it, we are developing a helicopter system, called Red-Eye, devoted to the detection, control and analysis of wild land forest fires in the Mediterranean area. The design of the proposed system is composed of five main components. Each component will work collaboratively to constitute a platform of high added value. The general architecture designed for wildfire monitoring is being tailored for two relevant objectives within the particular Mediterranean scenario: tactical day/night fire front evolution, and post-fire hot-spot detection.

Background oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. Results in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. Conclusion The tool is publicly available as a freeware at the address: http://www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA may be more appropriate in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics. The experimental results show that the presented method is able to invert unknown filters with good numerical results, even if only 100 samples or less are available.

Data from sensory shelf-life studies can be analyzed using survival statistical methods. The objective of this research was to introduce Bayesian methodology to sensory shelf-life studies and discuss its advantages in relation to classical (frequentist) methods. A specific algorithm which incorporates the interval censored data from shelf-life studies is presented. Calculations were applied to whole-fat and fat-free yogurt, each tasted by 80 consumers who answered ‘‘yes’’ or ‘‘no’’ to whether they would consume samples with different storage times. The Bayesian approach was used to obtain the failure functions and the estimated shelf lives of the yogurts. Two approaches were considered: first non-informative priors were used, and then results from the fat-free yogurt were used to construct a more informative prior for the whole-fat product. For both approaches a Weibull distribution was assumed. For the non-informative priors the results were analogous to those obtained by the classical methods. When an informative prior was used to estimate the whole-fat shelf-life parameters, there was a tendency to closer credible limits in the percentile distributions. Advantages of the Bayesian approach are discussed: no need for asymptotical assumptions and the possibility of improving the precision of the results by introducing external information.

Abstrac(- This paper shows the design of the IFFT module corresponding to the baseband processing of an OFDM transmitter according to the IEEE802.11a-g and Hiperlan/2 standard. This module will be included in a future OFDM demonstrator, which will be implemented into a progranunable logic device. We have used our own algorithm for IFFT computing. It is based on the recursive properties called decimation. This algorithm offers optimal characteristics for the hardware implementation: a high degree of parallelism and exactly the same interconnection pattern between any of the algorithm stages. A new point of view in the prototyping design flow and the verification process comes from the use of the last generation system level design environments for nsP" into FPGAs. These environments, called visual data flows, are ideally suited for modeling DSP systems sinc:e they allow a high level of functional abstraction with different data types and operators.

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.

This paper proposes a very fast method for blindly initial- izing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satis¯ed. The method can been used success- fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

This paper highlights the role of non-functional information when reusing from a component library. We describe a method for selecting appropriate implementations of Ada packages taking non-functional constraints into account; these constraints model the context of reuse. Constraints take the form of queries using an interface description language called NoFun, which is also used to state non-functional information in Ada packages; query results are trees of implementations, following the import relationships between components. We define two different situations when reusing components, depending whether we take the library being searched as closed or extendible. The resulting tree of implementations can be manipulated by the user to solve ambiguities, to state default behaviours, and by the like. As part of the proposal, we face the problem of computing from code the non-functional information that determines the selection process.

Several clinical studies have reported that EEG synchrony is affected by Alzheimer's disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann-Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6 Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8 Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6 Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

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

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer's disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied two state of the art multiway array decomposition (MAD) methods to extract unique features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral–spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE), and singular value decomposition (SVD) coupled to tensor unfolding. We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

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.

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.