Novel fast random search clustering approach for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs

In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman–Pearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios. RESUMEN. Método de separación ciega de fuentes para señales dispersas basado en la identificación de la matriz de mezcla mediante técnicas de "clustering" aleatorio.

Un modelo de encaminamiento en las redes móviles ad hoc con QoS

En la última década ha aumentado en gran medida el interés por las redes móviles Ad Hoc. La naturaleza dinámica y sin infraestructura de estas redes, exige un nuevo conjunto de algoritmos y estrategias para proporcionar un servicio de comunicación fiable extremo a extremo. En el contexto de las redes móviles Ad Hoc, el encaminamiento surge como una de las áreas más interesantes para transmitir información desde una fuente hasta un destino, con la calidad de servicio de extremo a extremo. Debido a las restricciones inherentes a las redes móviles, los modelos de encaminamiento tradicionales sobre los que se fundamentan las redes fijas, no son aplicables a las redes móviles Ad Hoc. Como resultado, el encaminamiento en redes móviles Ad Hoc ha gozado de una gran atención durante los últimos años. Esto ha llevado al acrecentamiento de numerosos protocolos de encaminamiento, tratando de cubrir con cada uno de ellos las necesidades de los diferentes tipos de escenarios. En consecuencia, se hace imprescindible estudiar el comportamiento de estos protocolos bajo configuraciones de red variadas, con el fin de ofrecer un mejor encaminamiento respecto a los existentes. El presente trabajo de investigación muestra precisamente una solución de encaminamiento en las redes móviles Ad Hoc. Dicha solución se basa en el mejoramiento de un algoritmo de agrupamiento y la creación de un modelo de encaminamiento; es decir, un modelo que involucra la optimización de un protocolo de enrutamiento apoyado de un mecanismo de agrupación. El algoritmo mejorado, denominado GMWCA (Group Management Weighted Clustering Algorithm) y basado en el WCA (Weighted Clustering Algorithm), permite calcular el mejor número y tamaño de grupos en la red. Con esta mejora se evitan constantes reagrupaciones y que los jefes de clústeres tengan más tiempo de vida intra-clúster y por ende una estabilidad en la comunicación inter-clúster. En la tesis se detallan las ventajas de nuestro algoritmo en relación a otras propuestas bajo WCA. El protocolo de enrutamiento Ad Hoc propuesto, denominado QoS Group Cluster Based Routing Protocol (QoSG-CBRP), utiliza como estrategia el empleo de clúster y jerarquías apoyada en el algoritmo de agrupamiento. Cada clúster tiene un jefe de clúster (JC), quien administra la información de enrutamiento y la envía al destino cuando esta fuera de su área de cobertura. Para evitar que haya constantes reagrupamientos y llamados al algoritmo de agrupamiento se consideró agregarle un jefe de cluster de soporte (JCS), el que asume las funciones del JC, siempre y cuando este haya roto el enlace con los otros nodos comunes del clúster por razones de alejamiento o por desgaste de batería. Matemáticamente y a nivel de algoritmo se han demostrado las mejoras del modelo propuesto, el cual ha involucrado el mejoramiento a nivel de algoritmo de clustering y del protocolo de enrutamiento. El protocolo QoSG-CBRP, se ha implementado en la herramienta de simulación Network Simulator 2 (NS2), con la finalidad de ser comparado con el protocolo de enrutamiento jerárquico Cluster Based Routing Protocol (CBRP) y con un protocolo de enrutamiento Ad Hoc reactivo denominado Ad Hoc On Demand Distance Vector Routing (AODV). Estos protocolos fueron elegidos por ser los que mejor comportamiento presentaron dentro de sus categorías. Además de ofrecer un panorama general de los actuales protocolos de encaminamiento en redes Ad Hoc, este proyecto presenta un procedimiento integral para el análisis de capacidades de la propuesta del nuevo protocolo con respecto a otros, sobre redes que tienen un alto número de nodos. Estas prestaciones se miden en base al concepto de eficiencia de encaminamiento bajo parámetros de calidad de servicio (QoS), permitiendo establecer el camino más corto posible entre un nodo origen y un nodo destino. Con ese fin se han realizado simulaciones con diversos escenarios para responder a los objetivos de la tesis. La conclusiones derivadas del análisis de los resultados permiten evaluar cualitativamente las capacidades que presenta el protocolo dentro del modelo propuesto, al mismo tiempo que avizora un atractivo panorama en líneas futuras de investigación. ABSTRACT In the past decade, the interest in mobile Ad Hoc networks has greatly increased. The dynamic nature of these networks without infrastructure requires a new set of algorithms and strategies to provide a reliable end-to-end communication service. In the context of mobile Ad Hoc networks, routing emerges as one of the most interesting areas for transmitting information from a source to a destination, with the quality of service from end-to-end. Due to the constraints of mobile networks, traditional routing models that are based on fixed networks are not applicable to Ad Hoc mobile networks. As a result, the routing in mobile Ad Hoc networks has experienced great attention in recent years. This has led to the enhancement of many routing protocols, trying to cover with each one of them, the needs of different types of scenarios. Consequently, it is essential to study the behavior of these protocols under various network configurations, in order to provide a better routing scheme. Precisely, the present research shows a routing solution in mobile Ad Hoc networks. This solution is based on the improvement of a clustering algorithm, and the creation of a routing model, ie a model that involves optimizing a routing protocol with the support of a grouping mechanism. The improved algorithm called GMWCA (Group Management Weighted Clustering Algorithm) and based on the WCA (Weighted Clustering Algorithm), allows to calculate the best number and size of groups in the network. With this enhancement, constant regroupings are prevented and cluster heads are living longer intra-cluster lives and therefore stability in inter-cluster communication. The thesis details the advantages of our algorithm in relation to other proposals under WCA. The Ad Hoc routing protocol proposed, called QoS Group Cluster Based Routing Protocol (QoSG-CBRP), uses a cluster-employment strategy and hierarchies supported by the clustering algorithm. Each cluster has a cluster head (JC), who manages the routing information and sends it to the destination when is out of your coverage area. To avoid constant rearrangements and clustering algorithm calls, adding a support cluster head (JCS) was considered. The JCS assumes the role of the JC as long as JC has broken the link with the other nodes in the cluster for common restraining reasons or battery wear. Mathematically and at an algorithm level, the improvements of the proposed model have been showed, this has involved the improvement level clustering algorithm and the routing protocol. QoSG-CBRP protocol has been implemented in the simulation tool Network Simulator 2 (NS2), in order to be compared with the hierarchical routing protocol Cluster Based Routing Protocol (CBRP) and with the reactive routing protocol Ad Hoc On Demand Distance Vector Routing (AODV). These protocols were chosen because they showed the best individual performance in their categories. In addition to providing an overview of existing routing protocols in Ad Hoc networks, this project presents a comprehensive procedure for capacity analysis of the proposed new protocol with respect to others on networks that have a high number of nodes. These benefits are measured based on the concept of routing efficiency under the quality of service (QoS) parameters, thus allowing for the shortest possible path between a source node and a destination node. To meet the objectives of the thesis, simulations have been performed with different scenarios. The conclusions derived from the analysis of the results to assess qualitatively the protocol capabilities presented in the proposed model, while an attractive scenario for future research appears.

Change factors that may influence sun and beach destinations in a low-growth scenario

Análisis de los principales factores de cambio que previsiblemente incidirán en los destinos turísticos de sol y playa en un escenario de bajo crecimiento.

Clustering techniques for patients suffering acquired brain injury inneuro personal trainer

The study of the effectiveness of the cognitive rehabilitation processes and the identification of cognitive profiles, in order to define comparable populations, is a controversial area, but concurrently it is strongly needed in order to improve therapies. There is limited evidence about cognitive rehabilitation efficacy. Many of the trials conclude that in spite of an apparent clinical good response, differences do not show statistical significance. The common feature in all these trials is heterogeneity among populations. In this situation, observational studies on very well controlled cohort of studies, together with innovative methods in knowledge extraction, could provide methodological insights for the design of more accurate comparative trials. Some correlation studies between neuropsychological tests and patients capacities have been carried out -1---2- and also correlation between tests and morphological changes in the brain -3-. The procedures efficacy depends on three main factors: the affectation profile, the scheduled tasks and the execution results. The relationship between them makes up the cognitive rehabilitation as a discipline, but its structure is not properly defined. In this work we present a clustering method used in Neuro Personal Trainer (NPT) to group patients into cognitive profiles using data mining techniques. The system uses these clusters to personalize treatments, using the patients assigned cluster to select which tasks are more suitable for its concrete needs, by comparing the results obtained in the past by patients with the same profile.

Assessing urban mobility through participatory scenario building and combined utility-regret assessment

La planificación de la movilidad sostenible urbana es una tarea compleja que implica un alto grado de incertidumbre debido al horizonte de planificación a largo plazo, la amplia gama de paquetes de políticas posibles, la necesidad de una aplicación efectiva y eficiente, la gran escala geográfica, la necesidad de considerar objetivos económicos, sociales y ambientales, y la respuesta del viajero a los diferentes cursos de acción y su aceptabilidad política (Shiftan et al., 2003). Además, con las tendencias inevitables en motorización y urbanización, la demanda de terrenos y recursos de movilidad en las ciudades está aumentando dramáticamente. Como consecuencia de ello, los problemas de congestión de tráfico, deterioro ambiental, contaminación del aire, consumo de energía, desigualdades en la comunidad, etc. se hacen más y más críticos para la sociedad. Esta situación no es estable a largo plazo. Para enfrentarse a estos desafíos y conseguir un desarrollo sostenible, es necesario considerar una estrategia de planificación urbana a largo plazo, que aborde las necesarias implicaciones potencialmente importantes. Esta tesis contribuye a las herramientas de evaluación a largo plazo de la movilidad urbana estableciendo una metodología innovadora para el análisis y optimización de dos tipos de medidas de gestión de la demanda del transporte (TDM). La metodología nueva realizado se basa en la flexibilización de la toma de decisiones basadas en utilidad, integrando diversos mecanismos de decisión contrariedad‐anticipada y combinados utilidad‐contrariedad en un marco integral de planificación del transporte. La metodología propuesta incluye dos aspectos principales: 1) La construcción de escenarios con una o varias medidas TDM usando el método de encuesta que incorpora la teoría “regret”. La construcción de escenarios para este trabajo se hace para considerar específicamente la implementación de cada medida TDM en el marco temporal y marco espacial. Al final, se construyen 13 escenarios TDM en términos del más deseable, el más posible y el de menor grado de “regret” como resultado de una encuesta en dos rondas a expertos en el tema. 2) A continuación se procede al desarrollo de un marco de evaluación estratégica, basado en un Análisis Multicriterio de Toma de Decisiones (Multicriteria Decision Analysis, MCDA) y en un modelo “regret”. Este marco de evaluación se utiliza para comparar la contribución de los distintos escenarios TDM a la movilidad sostenible y para determinar el mejor escenario utilizando no sólo el valor objetivo de utilidad objetivo obtenido en el análisis orientado a utilidad MCDA, sino también el valor de “regret” que se calcula por medio del modelo “regret” MCDA. La función objetivo del MCDA se integra en un modelo de interacción de uso del suelo y transporte que se usa para optimizar y evaluar los impactos a largo plazo de los escenarios TDM previamente construidos. Un modelo de “regret”, llamado “referencedependent regret model (RDRM)” (modelo de contrariedad dependiente de referencias), se ha adaptado para analizar la contribución de cada escenario TDM desde un punto de vista subjetivo. La validación de la metodología se realiza mediante su aplicación a un caso de estudio en la provincia de Madrid. La metodología propuesta define pues un procedimiento técnico detallado para la evaluación de los impactos estratégicos de la aplicación de medidas de gestión de la demanda en el transporte, que se considera que constituye una herramienta de planificación útil, transparente y flexible, tanto para los planificadores como para los responsables de la gestión del transporte. Planning sustainable urban mobility is a complex task involving a high degree of uncertainty due to the long‐term planning horizon, the wide spectrum of potential policy packages, the need for effective and efficient implementation, the large geographical scale, the necessity to consider economic, social, and environmental goals, and the traveller’s response to the various action courses and their political acceptability (Shiftan et al., 2003). Moreover, with the inevitable trends on motorisation and urbanisation, the demand for land and mobility in cities is growing dramatically. Consequently, the problems of traffic congestion, environmental deterioration, air pollution, energy consumption, and community inequity etc., are becoming more and more critical for the society (EU, 2011). Certainly, this course is not sustainable in the long term. To address this challenge and achieve sustainable development, a long‐term perspective strategic urban plan, with its potentially important implications, should be established. This thesis contributes on assessing long‐term urban mobility by establishing an innovative methodology for optimizing and evaluating two types of transport demand management measures (TDM). The new methodology aims at relaxing the utility‐based decision‐making assumption by embedding anticipated‐regret and combined utilityregret decision mechanisms in an integrated transport planning framework. The proposed methodology includes two major aspects: 1) Construction of policy scenarios within a single measure or combined TDM policy‐packages using the survey method incorporating the regret theory. The purpose of building the TDM scenarios in this work is to address the specific implementation in terms of time frame and geographic scale for each TDM measure. Finally, 13 TDM scenarios are built in terms of the most desirable, the most expected and the least regret choice by means of the two‐round Delphi based survey. 2) Development of the combined utility‐regret analysis framework based on multicriteria decision analysis (MCDA). This assessment framework is used to compare the contribution of the TDM scenario towards sustainable mobility and to determine the best scenario considering not only the objective utility value obtained from the utilitybased MCDA, but also a regret value that is calculated via a regret‐based MCDA. The objective function of the utility‐based MCDA is integrated in a land use and transport interaction model and is used for optimizing and assessing the long term impacts of the constructed TDM scenarios. A regret based model, called referente dependent regret model (RDRM) is adapted to analyse the contribution of each TDM scenario in terms of a subjective point of view. The suggested methodology is implemented and validated in the case of Madrid. It defines a comprehensive technical procedure for assessing strategic effects of transport demand management measures, which can be useful, transparent and flexible planning tool both for planners and decision‐makers.

Detecting false testimonies in reputation systems using self-organizing maps

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

PUE attack detection in CWSNs using anomaly detection techniques

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration.

INDICIa: a new distributed clustering protocol

Many data streaming applications produces massive amounts of data that must be processed in a distributed fashion due to the resource limitation of a single machine. We propose a distributed data stream clustering protocol. Theoretical analysis shows preliminary results about the quality of discovered clustering. In addition, we present results about the ability to reduce the time complexity respect to the centralized approach.

Regret theory-based scenario building using an interacttive process

This paper is an introduction of the regret theory-based scenario building approach combining with a modified Delphi method that uses an interactive process to design and assess four different TDM measures (i.e., cordon toll, parking charge, increased bus frequency and decreased bus fare). The case study of Madrid is used to present the analysis and provide policy recommendations. The new scenario building approach incorporates expert judgement and transport models in an interactive process. It consists of a two-round modified Delphi survey, which was answeared by a group of Spanish transport experts who were the participants of the Transport Engineering Congress (CIT 2012), and an integrated land-use and transport model (LUTI) for Madrid that is called MARS (Metropolitan Activity Relocation Simulator).

Clustering basado en redes bayesianas con predictoras continuas: aplicaciones en neurociencia

Desentrañar el funcionamiento del cerebro es uno de los principales desafíos a los que se enfrenta la ciencia actual. Un área de estudio que ha despertado muchas expectativas e interés es el análisis de la estructura cortical desde el punto de vista morfológico, de manera que se cree una simulación del cerebro a nivel molecular. Con ello se espera poder profundizar en el estudio de numerosas enfermedades neurológicas y patológicas. Con el desarrollo de este proyecto se persigue el estudio del soma y de las espinas desde el punto de vista de la neuromorfología teórica. Es común en el estado del arte que en el análisis de las características morfológicas de una neurona en tres dimensiones el soma sea ignorado o, en el mejor de los casos, que sea sustituido por una simple esfera. De hecho, el concepto de soma resulta abstracto porque no se dispone de una dfinición estricta y robusta que especifique exactamente donde finaliza y comienzan las dendritas. En este proyecto se alcanza por primera vez una definición matemática de soma para determinar qué es el soma. Con el fin de simular somas se ahonda en los atributos utilizados en el estado del arte. Estas propiedades, de índole genérica, no especifican una morfología única. Es por ello que se propone un método que agrupe propiedades locales y globales de la morfología. En disposición de las características se procede con la categorización del cuerpo celular en distintas clases a partir de un nuevo subtipo de red bayesiana dinámica adaptada al espacio. Con ello se discute la existencia de distintas clases de somas y se descubren las diferencias entre los somas piramidales de distintas capas del cerebro. A partir del modelo matemático se simulan por primera vez somas virtuales. Algunas morfologías de espinas han sido atribuidas a ciertos comportamientos cognitivos. Por ello resulta de interés dictaminar las clases existentes y relacionarlas con funciones de la actividad cerebral. La clasificación más extendida (Peters y Kaiserman-Abramof, 1970) presenta una definición ambigua y subjetiva dependiente de la interpretación de cada individuo y por tanto discutible. Este estudio se sustenta en un conjunto de descriptores extraídos mediante una técnica de análisis topológico local para representaciones 3D. Sobre estos datos se trata de alcanzar el conjunto de clases más adecuado en el que agrupar las espinas así como de describir cada grupo mediante reglas unívocas. A partir de los resultados, se discute la existencia de un continuo de espinas y las propiedades que caracterizan a cada subtipo de espina. ---ABSTRACT---Unravel how the brain works is one of the main challenges faced by current science. A field of study which has aroused great expectations and interest is the analysis of the cortical structure from a morphological point of view, so that a molecular level simulation of the brain is achieved. This is expected to deepen the study of many neurological and pathological diseases. This project seeks the study of the soma and spines from the theoretical neuromorphology point of view. In the state of the art it is common that when it comes to analyze the morphological characteristics of a three dimension neuron the soma is ignored or, in the best case, it is replaced by a simple sphere. In fact, the concept of soma is abstract because there is not a robust and strict definition on exactly where it ends and dendrites begin. In this project a mathematical definition is reached for the first time to determine what a soma is. With the aim to simulate somas the atributes applied in the state of the art are studied. These properties, generic in nature, do not specify a unique morphology. It is why it was proposed a method to group local and global morphology properties. In arrangement of the characteristics it was proceed with the categorization of the celular body into diferent classes by using a new subtype of dynamic Bayesian network adapted to space. From the result the existance of different classes of somas and diferences among pyramidal somas from distinct brain layers are discovered. From the mathematical model virtual somas were simulated for the first time. Some morphologies of spines have been attributed to certain cognitive behaviours. For this reason it is interesting to rule the existent classes and to relate them with their functions in the brain activity. The most extended classification (Peters y Kaiserman-Abramof, 1970) presents an ambiguous and subjective definition that relies on the interpretation of each individual and consequently it is arguable. This study was based on the set of descriptors extracted from a local topological analysis technique for 3D representations. On these data it was tried to reach the most suitable set of classes to group the spines as well as to describe each cluster by unambiguous rules. From these results, the existance of a continuum of spines and the properties that characterize each spine subtype were discussed .

A telecom analytics framework for dynamic quality of service management

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering

Recent advances in non-destructive imaging techniques, such as X-ray computed tomography (CT), make it possible to analyse pore space features from the direct visualisation from soil structures. A quantitative characterisation of the three-dimensional solid-pore architecture is important to understand soil mechanics, as they relate to the control of biological, chemical, and physical processes across scales. This analysis technique therefore offers an opportunity to better interpret soil strata, as new and relevant information can be obtained. In this work, we propose an approach to automatically identify the pore structure of a set of 200-2D images that represent slices of an original 3D CT image of a soil sample, which can be accomplished through non-linear enhancement of the pixel grey levels and an image segmentation based on a PFCM (Possibilistic Fuzzy C-Means) algorithm. Once the solids and pore spaces have been identified, the set of 200-2D images is then used to reconstruct an approximation of the soil sample by projecting only the pore spaces. This reconstruction shows the structure of the soil and its pores, which become more bounded, less bounded, or unbounded with changes in depth. If the soil sample image quality is sufficiently favourable in terms of contrast, noise and sharpness, the pore identification is less complicated, and the PFCM clustering algorithm can be used without additional processing; otherwise, images require pre-processing before using this algorithm. Promising results were obtained with four soil samples, the first of which was used to show the algorithm validity and the additional three were used to demonstrate the robustness of our proposal. The methodology we present here can better detect the solid soil and pore spaces on CT images, enabling the generation of better 2D?3D representations of pore structures from segmented 2D images.

Moldels for reproducing the damage scenario of the Lorca earthquake

A damage scenario modelling is developed and compared with the damage distribution observed after the 2011 Lorca earthquake. The strong ground motion models considered include five modern ground motion prediction equations (GMPEs) amply used worldwide. Capacity and fragility curves from the Risk-UE project are utilized to model building vulnerability and expected damage. Damage estimates resulting from different combinations of GMPE and capacity/fragility curves are compared with the actual damage scenario, establishing the combination that best explains the observed damage distribution. In addition, some recommendations are proposed, including correction factors in fragility curves in order to reproduce in a better way the observed damage in masonry and reinforce concrete buildings. The lessons learned would contribute to improve the simulation of expected damages due to future earthquakes in Lorca or other regions in Spain with similar characteristics regarding attenuation and vulnerability.

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.