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  • Hierarchical information representation and efficient classification of gene expression microarray data  Open access

     Bosio, Mattia
    Defense's date: 2014-06-27
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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    En el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras.La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filtrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula.El objetivo de esta tesis es mejorar el estado del arte en la clasificación de microarrays y contribuir a entender como se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasificación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmos desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables.La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con fi ensembles". El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza ¿ensembles" aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays.El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados.

    In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet. Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process. This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk. The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers. In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions. All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art. Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.

    En el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras. La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filltrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula. El objetivo de esta tesis es mejorar el estado del arte en la clasi cación de microarrays y contribuir a entender cómo se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasi cación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmo desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables. La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con “ensembles”. El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza “ensembles” aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays. El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados.

  • Hierarchical clustering combining numerical and biological similarities for gene expression data classification

     Bosio, Mattia; Salembier Clairon, Philippe Jean; Bellot Pujalte, Pau; Oliveras Verges, Albert
    IEEE Engineering in Medicine and Biology Society
    Presentation's date: 2013-07
    Presentation of work at congresses

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    High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.

  • Ensemble learning and hierarchical data representation for microarray classification

     Bosio, Mattia; Bellot, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Conference on Bioinformatics and Bioengineering
    Presentation's date: 2013-11-11
    Presentation of work at congresses

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    The microarray data classification is an open and active research field. The development of more accurate algorithms is of great interest and many of the developed techniques can be straightforwardly applied in analyzing different kinds of omics data. In this work, an ensemble learning algorithm is applied within a classification framework that already got good predictive results. Ensemble techniques take individual experts, (i.e. classifiers), to combine them to improve the individual expert results with a voting scheme. In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote. Two versions of a state of the art ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC. The proposed key elements have shown to be useful for the prediction performance and the studied ensemble technique shown to improve the state of the art results by producing classifiers with better predictions.

    The microarray data classification is an open and active research field. The development of more accurate algorithms is of great interest and many of the developed techniques can be straightforwardly applied in analyzing different kinds of omics data. In this work, an ensemble learning algorithm is applied within a classification framework that already got good predictive results. Ensemble techniques take individual experts, (i.e. classifiers), to combine them to improve the individual expert results with a voting scheme. In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote. Two versions of a state of the art ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC. The proposed key elements have shown to be useful for the prediction performance and the studied ensemble technique shown to improve the state of the art results by producing classifiers with better predictions.

  • Access to the full text
    Designing CDIO capstone projects: a systems thinking approach  Open access

     Alarcon Cot, Eduardo Jose; Bou Balust, Elisenda; Camps Carmona, Adriano Jose; Bragos Bardia, Ramon; Oliveras Verges, Albert; Pegueroles Valles, Josep Rafel; Sayrol Clols, Elisa; Marques Acosta, Fernando
    International CDIO Conference
    Presentation's date: 2013-06-10
    Presentation of work at congresses

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    Given the all-pervasiveness of Systems thinking -which consists of thinking about things as systems- as a way of reasoning, in this work we will describe its application to make an interpretation of how to conceive and design a final year CDIO capstone course. Both the student teamwork structure as well as the complex engineering system itself addressed in the project are described in terms of entities, links, form and function, thereby pointing out their formal and functional interaction. The ultimate goal of the Systems thinking perspective is, given the necessary ingredients, to try maximizing the chances of the emergence of a fruitful capstone course, namely a culminating project that yields a set of students qualified to CDIO complex engineering systems.

    Given the all-pervasiveness of Systems thinking -which consists of thinking about things as systemsas a way of reasoning, in this work we will describe its application to make an interpretation of how to conceive and design a final year CDIO capstone course. Both the student teamwork structure as well as the complex engineering system itself addressed in the project are described in terms of entities, links, form and function, thereby pointing out their formal and functional interaction. The ultimate goal of the Systems thinking perspective is, given the necessary ingredients, to try maximizing the chances of the emergence of a fruitful capstone course, namely a culminating project that yields a set of students qualified to CDIO complex engineering systems.

  • Gene expression data classification combining hierarchical representation and efficient feature selection

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    Journal of biological systems
    Date of publication: 2012-12
    Journal article

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    A general framework for microarray data classification is proposed in this paper. It pro- duces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the com- mon behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of func- tionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process. The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). The comparative results showed that the proposed framework can compete with a wide variety of state of the art alternatives and it can obtain the best mean performance if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed framework obtains stable and repeatable results.

  • Multiclass cancer-microarray classification algorithm with Pair-Against-All redundancy

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Workshop on Genomic Signal Processing and Statistic
    Presentation's date: 2012-12-03
    Presentation of work at congresses

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    Multiclass cancer classification is still a challenging task in the field of machine learning. A novel multiclass approach is proposed in this work as a combination of multiple binary classifiers. It is an example of Error Correcting Output Codes algorithms, applying data transmission coding techniques to improve the classification as a combination of binary classifiers. The proposed method combines the One Against All, OAA, approach with a set of classifiers separating each class-pair from the rest, called Pair Against All, PAA. The OAA+PAA approach has been tested on seven publicly available datasets. It has been compared with the common OAA approach and with state of the art alternatives. The obtained results showed how the OAA+PAA algorithm consistently improves the OAA results, unlike other ECOC algorithms presented in the literature.

  • Microarray classification with hierarchical data representation and novel feature selection criteria

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Conference on BioInformatics and BioEngineering
    Presentation's date: 2012-11-12
    Presentation of work at congresses

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    Microarray data classification is a challenging problem due to the high number of variables compared to the small number of available samples. An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1]. It considers the sample scarcity problem and the lack of data structure typical of microarrays. Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes and introducing a novel feature ranking criterion, inside the wrapper feature selection task. The classification ability has been evaluated on 4 publicly available datasets from Micro Array Quality Control study phase II (MAQC) classified by 7 different endpoints. The global results have showed how the proposed approach obtains better prediction accuracy than a wide variety of state of the art alternatives.

    Microarray data classification is a challenging prob- lem due to the high number of variables compared to the small number of available samples. An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1]. It considers the sample scarcity problem and the lack of data structure typical of microarrays. Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes and introducing a novel feature ranking criterion, inside the wrapper feature selection task. The classification ability has been evaluated on 4 publicly available datasets from Micro Array Quality Control study phase II (MAQC) classified by 7 different endpoints. The global results have showed how the proposed approach obtains better prediction accuracy than a wide variety of state of the art alternatives

  • Distincio Jaume Vicenç Vives

     Bragos Bardia, Ramon; Alarcon Cot, Eduardo Jose; Camps Carmona, Adriano Jose; Consolacion Segura, Carolina Maria; Oliveras Verges, Albert; Pegueroles Valles, Josep Rafel; Sayrol Clols, Elisa
    Award or recognition

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  • 15è. Premi UPC a la Qualitat en la Docència Universitària

     Bragos Bardia, Ramon; Sayrol Clols, Elisa; Alarcon Cot, Eduardo Jose; Camps Carmona, Adriano Jose; Pegueroles Valles, Josep Rafel; Oliveras Verges, Albert; Consolacion Segura, Carolina Maria
    Award or recognition

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  • Implementation and first results of the Introduction to Engineering course in the ETSETB-UPC new degrees

     Bragos Bardia, Ramon; Pergueroles, Josep; Alarcon Cot, Eduardo Jose; Camps Carmona, Adriano Jose; Sarda Ferrer, Joan; Consolacion Segura, Carolina Maria; Mussons Selles, Jaume; Pons Peregort, Olga; Oliveras Verges, Albert; García, Miguel; Onrubia Ibañez, Raul; Sayrol Clols, Elisa
    Conferencia Internacional de Fomento e Innovación con Nuevas Tecnologías en la Docencia de la Ingeniería
    Presentation of work at congresses

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    This paper describes the new course "Introduction to ICT Engineering" which has been conceived, designed and implemented at Telecom-BCN (ETSETB-UPC) from initial specifications and restrictions. It is the first of a series of four project subjects distributed throughout the new curricula. It is organized in three parallel paths covering the systemic vision of complex ICT systems, the basic concepts of economics, business and project management and the physical realization of a design-build project. In the first 5 years, this project is beling performed using the SeaPerch platform, a small underwater robot developed at MIT Sea Grant. A payload which includes measurement and communications subsystems is designed and built. After testing with small groups for two semesters, the course will be undertaken with around 250 students in the spring semester of this year.

  • Implementación y primeros resultados de la asignatura de introducción a la ingeniería en los nuevos grados de la ETSETB-UPC

     Bragos Bardia, Ramon; Pegueroles Valles, Josep Rafel; Alarcon Cot, Eduardo Jose; Camps Carmona, Adriano Jose; Sarda Ferrer, Joan; Consolacion Segura, Carolina Maria; Mussons Selles, Jaume; Pons Peregort, Olga; Oliveras Verges, Albert; Garcia Hernandez, Miguel Jesus; Onrubia Ibañez, Raul; Sayrol Clols, Elisa
    Conferencia Internacional de Fomento e Innovación con Nuevas Tecnologías en la Docencia de la Ingeniería
    Presentation's date: 2011-05-05
    Presentation of work at congresses

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    En este trabajo se describe la asignatura "Introducción a la Ingeniería de las TIC" que, basándose en restricciones y especificaciones iniciales, ha sido concebida, diseñada e implementada en la ETSETB de la UPC. Es la primera de una serie de cuatro asignaturas de proyectos distribuidas a lo largo de los grados. Está organizada en tres itinerarios paralelos que cubren la visión de sistema TIC complejo, los conceptos básicos de economía, empresa y gestión de proyectos y la realización física de un proyecto. En los primeros 5 años este proyecto se lleva a cabo sobre la plataforma SeaPerch, un pequeño robot subacuático desarrollado en el MIT Sea Grant, sobre el que se diseñan y construyen sistemas de medida y comunicaciones. Después de probarla con grupos pequeños durante dos cuatrimestres, se acomete su explotación con una previsión de 250 estudiantes en el cuatrimestre de primavera de este año.

  • Feature set enhancement via hierarchical clustering for microarray classification

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Workshop on Genomic Signal Processing and Statistics
    Presentation's date: 2011
    Presentation of work at congresses

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  • Procesado de vídeo multicámara empleando información de la escena: aplicación a eventos deportivos, interacción visual y 3DTV

     Giro Nieto, Xavier; Oliveras Verges, Albert; Gasull Llampallas, Antoni; Salembier Clairon, Philippe Jean; Marques Acosta, Fernando; Sayrol Clols, Elisa; Pardas Feliu, Montserrat; Morros Rubió, Josep Ramon; Ruiz Hidalgo, Javier; Vilaplana Besler, Veronica; Casas Pla, Josep Ramon
    Participation in a competitive project

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  • Adquisición multicámara para Free Viewpoint Video (MC4FVV)

     Pardas Feliu, Montserrat; Giro Nieto, Xavier; Vilaplana Besler, Veronica; Ruiz Hidalgo, Javier; Morros Rubió, Josep Ramon; Salembier Clairon, Philippe Jean; Marques Acosta, Fernando; Gasull Llampallas, Antoni; Oliveras Verges, Albert; Sayrol Clols, Elisa; Casas Pla, Josep Ramon
    Participation in a competitive project

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  • GRUP DE PROECESSAMENT D'IMATGE I VIDEO (GPI)

     Vilaplana Besler, Veronica; Giro Nieto, Xavier; Ruiz Hidalgo, Javier; Oliveras Verges, Albert; Marques Acosta, Fernando; Sayrol Clols, Elisa; Pardas Feliu, Montserrat; Morros Rubió, Josep Ramon; Gasull Llampallas, Antoni; Salembier Clairon, Philippe Jean; Casas Pla, Josep Ramon
    Participation in a competitive project

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  • Maximum likelihood factor analysis in malaria cytokines analysis and modelling

     Oliveras Verges, Albert
    IEEE International Workshop on Genomic Signal Processing and Statistics
    Presentation's date: 2009-05-18
    Presentation of work at congresses

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  • Procesado de vídeo en entornos controlados: aplicación a seguridad, salas inteligentes y telepresencia (PROVEC)

     Casas Pla, Josep Ramon; Gasull Llampallas, Antoni; Giro Nieto, Xavier; Marques Acosta, Fernando; Morros Rubió, Josep Ramon; Oliveras Verges, Albert; Pardas Feliu, Montserrat; Ruiz Hidalgo, Javier; Salembier Clairon, Philippe Jean; Sayrol Clols, Elisa; Vilaplana Besler, Veronica
    Participation in a competitive project

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  • 2005SGR-00341 GRUP DE PROCESSAMENT D'IMATGE I VIDEO

     Gasull Llampallas, Antoni; Casas Pla, Josep Ramon; Giro Nieto, Xavier; Marques Acosta, Fernando; Morros Rubió, Josep Ramon; Oliveras Verges, Albert; Pardas Feliu, Montserrat; Ruiz Hidalgo, Javier; Salembier Clairon, Philippe Jean; Sayrol Clols, Elisa; Vilaplana Besler, Veronica
    Participation in a competitive project

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  • 2001SGR-00265 GRUP DE TRACTAMENT DE LA IMATGE

     Casas Pla, Josep Ramon; Gasull Llampallas, Antoni; Giro Nieto, Xavier; Marques Acosta, Fernando; Morros Rubió, Josep Ramon; Oliveras Verges, Albert; Pardas Feliu, Montserrat; Ruiz Hidalgo, Javier; Salembier Clairon, Philippe Jean; Vilaplana Besler, Veronica
    Participation in a competitive project

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  • Editor gráfico de figuras MATLAB

     Gasull Llampallas, Antoni; Sayrol Clols, Elisa; Moreno Bilbao, M. Asuncion; Vallverdu Bayes, Francisco; Salavedra Moli, Josep; Oliveras Verges, Albert
    III Congreso de Usuarios de MATLAB
    Presentation of work at congresses

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  • Simulación Digital de Señales y Sistemas Analógicos

     Sayrol Clols, Elisa; Gasull Llampallas, Antoni; Salavedra Moli, Josep; Moreno Bilbao, M. Asuncion; Vallverdu Bayes, Francisco; Oliveras Verges, Albert
    III Congreso de Usuarios de MATLAB
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  • Editor Gráfico de Figuras MATLAB

     Gasull Llampallas, Antoni; Sayrol Clols, Elisa; Moreno Bilbao, M. Asuncion; Vallverdu Bayes, Francisco; Salavedra Moli, Josep; Oliveras Verges, Albert
    III Congreso de Usuarios de MATLAB
    Presentation of work at congresses

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  • Simulación digital de señales y sistemas analógicos

     Sayrol Clols, Elisa; Gasull Llampallas, Antoni; Moreno Bilbao, M. Asuncion; Vallverdu Bayes, Francisco; Salavedra Moli, Josep; Oliveras Verges, Albert
    III Congreso de Usuarios de MATLAB
    Presentation of work at congresses

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  • Antiextensive connected operators for image and sequence processing

     Salembier Clairon, Philippe Jean; Oliveras Verges, Albert; Garrido Ostermann, Luis
    IEEE transactions on image processing
    Date of publication: 1998-04
    Journal article

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  • Morphological tools for segmentation: connected operators and watersheds

     Meyer, F; Oliveras Verges, Albert; Salembier Clairon, Philippe Jean; Vachier, C
    Annales des télecommunications. Annals of telecommunications
    Date of publication: 1997-08
    Journal article

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  • Contribució a l'anàlisi morfològica d'imatges amb operadors connexes.

     Oliveras Verges, Albert
    Defense's date: 1997-07-31
    Department of Automatic Control, Universitat Politècnica de Catalunya
    Theses

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  • Stereo Image Analysis using Connected Operators

     Oliveras Verges, Albert; Salembier Clairon, Philippe Jean; Garrido Ostermann, Luis
    IEEE INTERNATIONAL CONFERENCE ON ACOUSTIC, SPEECH AND SIGNAL PROCESSING (ICASSP'97)
    Presentation of work at congresses

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  • Anti-extensive Connected Operators with Application to Image Sequences

     Garrido Ostermann, Luis; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes
    Presentation's date: 1997-04-24
    Presentation of work at congresses

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  • PRESENTACIÓ DEL POSTER " ANALYSIS OF STEREO IMAGES USING CONNECTED OPERATORS"

     Oliveras Verges, Albert
    IEEE INTERNATIONAL CONFERENCE ON ACOUSTIC, SPEECH AND SIGNAL PROCESSING (ICASSP'97)
    Presentation's date: 1997-04-21
    Presentation of work at congresses

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  • Motion analysis of image sequences using connected operators

     Garrido Ostermann, Luis; Oliveras Verges, Albert; Salembier Clairon, Philippe Jean
    SPIE VISUAL COMMUNICATIONS AND IMAGE PROCESSING, VCIP '97
    Presentation of work at congresses

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  • Analysis of stereo images using connected operators

     Oliveras Verges, Albert; GARRIDO, LUIS; Salembier Clairon, Philippe Jean
    IEEE INTERNATIONAL CONFERENCE ON ACOUSTIC, SPEECH AND SIGNAL PROCESSING (ICASSP'97)
    Presentation of work at congresses

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  • Complexity connected operators

     Oliveras Verges, Albert; Salembier Clairon, Philippe Jean
    SPIE'S 1996 SYMPOSIUM ON "VISUAL COMMUNICATIONS AND IMAGE PROCESSING '96"
    Presentation of work at congresses

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  • FILTERING METHOD AND CORRESPONDING FILTERING SYSTEM

     Oliveras Verges, Albert; Salembier Clairon, Philippe Jean; GARRIDO, LUIS
    Date of request: 1996-03-13
    Invention patent

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  • CONNECTED OPERATORS WITH APPLICATION TO COMPLEXITY AND MOTION

     Salembier Clairon, Philippe Jean; Oliveras Verges, Albert; Garrido, L
    Date: 1996-05
    Report

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  • COMPLEXITY CONNECTED OPERATORS

     Oliveras Verges, Albert
    SPIE'S 1996 SYMPOSIUM ON "VISUAL COMMUNICATIONS AND IMAGE PROCESSING '96"
    Presentation's date: 1996-03-17
    Presentation of work at congresses

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  • Motion connected operators for image sequences

     Salembier Clairon, Philippe Jean; Oliveras Verges, Albert; Garrido, L
    European Signal Processing Conference
    Presentation of work at congresses

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  • Generalized connected operators

     Oliveras Verges, Albert; Salembier Clairon, Philippe Jean
    VISUAL COMMUNICATION AND IMAGE PROCESSING
    Presentation of work at congresses

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  • Practical extensions of connected operators

     Oliveras Verges, Albert; Salembier Clairon, Philippe Jean
    WORKSHOP ON MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO IMAGE
    Presentation of work at congresses

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  • TWO LEVEL CONTINUOUS SPEECH RECOGNITION USING DEMISYLLABLE BASED HMM WORD SPOT

     Oliveras Verges, Albert
    EUROPEAN CONF. ON SPEECH COMMUN. AND TECHN. EUROSPEECH'91
    Presentation's date: 1991-09-01
    Presentation of work at congresses

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  • RECONOCIMIENTO DEL HABLA

     Lleida Solano, Eduardo; Oliveras Verges, Albert
    Date: 1991-05
    Report

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  • Tou - Brazo Robot Asistencial: Control Verbal

     Oliveras Verges, Albert; Fuertes Armengol, José Mª; Villà, R
    2º Congreso de la Asociación Española de Robótica
    Presentation of work at congresses

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  • Two level c.s.r. using demisyllable..

     Lleida Solano, Eduardo; Oliveras Verges, Albert; Nadeu Camprubí, Climent; Mariño Acebal, Jose Bernardo
    EUROPEAN CONF. ON SPEECH COMMUN. AND TECHN. EUROSPEECH'91
    Presentation of work at congresses

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  • Simbad:a tool for speech analysis and synthesis

     Nadeu Camprubí, Climent; Oliveras Verges, Albert; Mariño Acebal, Jose Bernardo
    IASTED INT.CONF.SIGNAL PROC.&DIG.FILT.
    Presentation of work at congresses

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