Cadmium Selenide Based Core-Shell Quantum Dots for Biosensing and Imaging Applications

The overall focus of the thesis involves the synthesis and characterization of CdSe QDs overcoated with shell materials for various biological and chemical sensing applications. Second chapter deals with the synthesis and characterization of CdSe and CdSe/ZnS core shell QDs. The primary attention of this work is to develop a simple method based on photoinduced charge transfer to optimize the shell thickness. Synthesis of water soluble CdSe QDs, their cytotoxicity analysis and investigation of nonlinear optical properties form the subject of third chapter. Final chapter deals with development of QD based sensor systems for the selective detection of biologically and environmentally important analytes from aqueous media.

A location aided cooperative caching protocol for mobile ad hoc networks

Cooperative caching is an attractive solution for reducing bandwidth demands and network latency in mobile ad hoc networks. Deploying caches in mobile nodes can reduce the overall traffic considerably. Cache hits eliminate the need to contact the data source frequently, which avoids additional network overhead. In this paper we propose a data discovery and cache management policy for cooperative caching, which reduces the caching overhead and delay by reducing the number of control messages flooded in to the network. A cache discovery process based on location of neighboring nodes is developed for this. The cache replacement policy we propose aims at increasing the cache hit ratio. The simulation results gives a promising result based on the metrics of studies

Analytical Study of Light Propagation in Highly Nonlinear Media

The present dissertation is devoted to the construction of exact and approximate analytical solutions of the problem of light propagation in highly nonlinear media. It is demonstrated that for many experimental conditions, the problem can be studied under the geometrical optics approximation with a sufficient accuracy. Based on the renormalization group symmetry analysis, exact analytical solutions of the eikonal equations with a higher order refractive index are constructed. A new analytical approach to the construction of approximate solutions is suggested. Based on it, approximate solutions for various boundary conditions, nonlinear refractive indices and dimensions are constructed. Exact analytical expressions for the nonlinear self-focusing positions are deduced. On the basis of the obtained solutions a general rule for the single filament intensity is derived; it is demonstrated that the scaling law (the functional dependence of the self-focusing position on the peak beam intensity) is defined by a form of the nonlinear refractive index but not the beam shape at the boundary. Comparisons of the obtained solutions with results of experiments and numerical simulations are discussed.

Example Based Learning for View-Based Human Face Detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system.

Uncertainty Propagation in Model-Based Recognition

Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Two methods for voltage sag source location

This paper presents and compares two approaches to estimate the origin (upstream or downstream) of voltage sag registered in distribution substations. The first approach is based on the application of a single rule dealing with features extracted from the impedances during the fault whereas the second method exploit the variability of waveforms from an statistical point of view. Both approaches have been tested with voltage sags registered in distribution substations and advantages, drawbacks and comparative results are presented

Application for fault location in electrical power distribution systems

Fault location has been studied deeply for transmission lines due to its importance in power systems. Nowadays the problem of fault location on distribution systems is receiving special attention mainly because of the power quality regulations. In this context, this paper presents an application software developed in Matlabtrade that automatically calculates the location of a fault in a distribution power system, starting from voltages and currents measured at the line terminal and the model of the distribution power system data. The application is based on a N-ary tree structure, which is suitable to be used in this application due to the highly branched and the non- homogeneity nature of the distribution systems, and has been developed for single-phase, two-phase, two-phase-to-ground, and three-phase faults. The implemented application is tested by using fault data in a real electrical distribution power system

WAIS seminar: Linked Cultural Media

Wednesday 9th April 2014 Speaker(s): Guus Schreiber Time: 09/04/2014 11:00-11:50 Location: B32/3077 File size: 546Mb Abstract In this talk I will discuss linked data for museums, archives and libraries. This area is known for its knowledge-rich and heterogeneous data landscape. The objects in this field range from old manuscripts to recent TV programs. Challenges in this field include common metadata schema's, inter-linking of the omnipresent vocabularies, cross-collection search strategies, user-generated annotations and object-centric versus event-centric views of data. This work can be seen as part of the rapidly evolving field of digital humanities. Speaker Biography Guus Schreiber Guus is a professor of Intelligent Information Systems at the Department of Computer Science at VU University Amsterdam. Guus’ research interests are mainly in knowledge and ontology engineering with a special interest for applications in the field of cultural heritage. He was one of the key developers of the CommonKADS methodology. Guus acts as chair of W3C groups for Semantic Web standards such as RDF, OWL, SKOS and REFa. His research group is involved in a wide range of national and international research projects. He is now project coordinator of the EU Integrated project No Tube concerned with integration of Web and TV data with the help of semantics and was previously Scientific Director of the EU Network of Excellence “Knowledge Web”.

WAIS Seminar:Let's SoFWIReD!

Title: Let’s SoFWIReD! Time: Wed, 21 May 2014 11:00-11:50 Location: Building 32, Room 3077 Speaker: Dr Sepi Chakaveh Abstract The information age as we know it has its roots in several enabling technologies – most of all the World Wide Web – for the provision of truly global connectivity. The emergence of a Web of Big Data in terms of the publication and analysis of Open Data provides new insights about the impact of the Web in our society. The second most important technology in this regard has been the emergence of streaming processes based on new and innovative compression methods such as MP3 so that audio and video content becomes accessible to everyone on the Web. The SoFWIReD team is developing comprehensive, interoperable platforms for data and knowledge driven processing of Open Data and will investigate aspects of collective intelligence. Insights generated in the project will form the basis for supporting companies through consulting, organisational development, and software solutions so that they can master the collective intelligence transition. The seminar will present how the project addresses the research topics of web observatory, dynamic media objects, crowd-sourced open data and Internet services. At the end of a talk a number of demos will be shown in the context of SoFWIReD’s Dynamic Media Object.

Modelación estocástica y trading algorítmico del spread entre acciones mediante procesos de reversión a la media

Las estrategias de inversión pairs trading se basan en desviaciones del precio entre pares de acciones correlacionadas y han sido ampliamente implementadas por fondos de inversión tomando posiciones largas y cortas en las acciones seleccionadas cuando surgen divergencias y obteniendo utilidad cerrando la posición al converger. Se describe un modelo de reversión a la media para analizar la dinámica que sigue el diferencial del precio entre acciones ordinarias y preferenciales de una misma empresa en el mismo mercado. La media de convergencia en el largo plazo es obtenida con un filtro de media móvil, posteriormente, los parámetros del modelo de reversión a la media se estiman mediante un filtro de Kalman bajo una formulación de estado espacio sobre las series históricas. Se realiza un backtesting a la estrategia de pairs trading algorítmico sobre el modelo propuesto indicando potenciales utilidades en mercados financieros que se observan por fuera del equilibrio. Aplicaciones de los resultados podrían mostrar oportunidades para mejorar el rendimiento de portafolios, corregir errores de valoración y sobrellevar mejor periodos de bajos retornos.

Tipos de lógica utilizadas en la toma de decisiones estratégicas de marketing

Para el administrador el proceso de la toma de decisiones es uno de sus mayores retos y responsabilidades, ya que en su desarrollo se debe definir el camino más acertado en un sin número de alternativas, teniendo en cuenta los obstáculos sociales, políticos y económicos del entorno empresarial. Para llegar a la decisión adecuada no hay que perder de vista los objetivos y metas propuestas, además de tener presente el proceso lógico, detectando, analizando y demostrando el porqué de esa elección. Consecuentemente el análisis que propone esta investigación aportara conocimientos sobre los tipos de lógica utilizados en la toma de decisiones estratégicas al administrador para satisfacer las demandas asociadas con el mercadeo para que de esta manera se pueda generar y ampliar eficientemente las competencia idóneas del administrador en la inserción internacional de un mercado laboral cada vez mayor (Valero, 2011). A lo largo de la investigación se pretende desarrollar un estudio teórico para explicar la relación entre la lógica y la toma de decisiones estratégicas de marketing y como estos conceptos se combinan para llegar a un resultado final. Esto se llevara a cabo por medio de un análisis de planes de marketing, iniciando por conceptos básicos como marketing, lógica, decisiones estratégicas, dirección de marketing seguido de los principios lógicos y contradicciones que se pueden llegar a generar entre la fundamentación teórica

Development of molecular-based techniques for the detection, identification and quantification of food-borne pathogens

La presencia de microorganismos patógenos en alimentos es uno de los problemas esenciales en salud pública, y las enfermedades producidas por los mismos es una de las causas más importantes de enfermedad. Por tanto, la aplicación de controles microbiológicos dentro de los programas de aseguramiento de la calidad es una premisa para minimizar el riesgo de infección de los consumidores. Los métodos microbiológicos clásicos requieren, en general, el uso de pre-enriquecimientos no-selectivos, enriquecimientos selectivos, aislamiento en medios selectivos y la confirmación posterior usando pruebas basadas en la morfología, bioquímica y serología propias de cada uno de los microorganismos objeto de estudio. Por lo tanto, estos métodos son laboriosos, requieren un largo proceso para obtener resultados definitivos y, además, no siempre pueden realizarse. Para solucionar estos inconvenientes se han desarrollado diversas metodologías alternativas para la detección identificación y cuantificación de microorganismos patógenos de origen alimentario, entre las que destacan los métodos inmunológicos y moleculares. En esta última categoría, la técnica basada en la reacción en cadena de la polimerasa (PCR) se ha convertido en la técnica diagnóstica más popular en microbiología, y recientemente, la introducción de una mejora de ésta, la PCR a tiempo real, ha producido una segunda revolución en la metodología diagnóstica molecular, como pude observarse por el número creciente de publicaciones científicas y la aparición continua de nuevos kits comerciales. La PCR a tiempo real es una técnica altamente sensible -detección de hasta una molécula- que permite la cuantificación exacta de secuencias de ADN específicas de microorganismos patógenos de origen alimentario. Además, otras ventajas que favorecen su implantación potencial en laboratorios de análisis de alimentos son su rapidez, sencillez y el formato en tubo cerrado que puede evitar contaminaciones post-PCR y favorece la automatización y un alto rendimiento. En este trabajo se han desarrollado técnicas moleculares (PCR y NASBA) sensibles y fiables para la detección, identificación y cuantificación de bacterias patogénicas de origen alimentario (Listeria spp., Mycobacterium avium subsp. paratuberculosis y Salmonella spp.). En concreto, se han diseñado y optimizado métodos basados en la técnica de PCR a tiempo real para cada uno de estos agentes: L. monocytogenes, L. innocua, Listeria spp. M. avium subsp. paratuberculosis, y también se ha optimizado y evaluado en diferentes centros un método previamente desarrollado para Salmonella spp. Además, se ha diseñado y optimizado un método basado en la técnica NASBA para la detección específica de M. avium subsp. paratuberculosis. También se evaluó la aplicación potencial de la técnica NASBA para la detección específica de formas viables de este microorganismo. Todos los métodos presentaron una especificidad del 100 % con una sensibilidad adecuada para su aplicación potencial a muestras reales de alimentos. Además, se han desarrollado y evaluado procedimientos de preparación de las muestras en productos cárnicos, productos pesqueros, leche y agua. De esta manera se han desarrollado métodos basados en la PCR a tiempo real totalmente específicos y altamente sensibles para la determinación cuantitativa de L. monocytogenes en productos cárnicos y en salmón y productos derivados como el salmón ahumado y de M. avium subsp. paratuberculosis en muestras de agua y leche. Además este último método ha sido también aplicado para evaluar la presencia de este microorganismo en el intestino de pacientes con la enfermedad de Crohn's, a partir de biopsias obtenidas de colonoscopia de voluntarios afectados. En conclusión, este estudio presenta ensayos moleculares selectivos y sensibles para la detección de patógenos en alimentos (Listeria spp., Mycobacterium avium subsp. paratuberculosis) y para una rápida e inambigua identificación de Salmonella spp. La exactitud relativa de los ensayos ha sido excelente, si se comparan con los métodos microbiológicos de referencia y pueden serusados para la cuantificación de tanto ADN genómico como de suspensiones celulares. Por otro lado, la combinación con tratamientos de preamplificación ha resultado ser de gran eficiencia para el análisis de las bacterias objeto de estudio. Por tanto, pueden constituir una estrategia útil para la detección rápida y sensible de patógenos en alimentos y deberían ser una herramienta adicional al rango de herramientas diagnósticas disponibles para el estudio de patógenos de origen alimentario.