Implantación de un sistema VoIP basado en software libre con Asterisk

Este proyecto busca analizar, diseñar e implementar una nueva solución de telefonía para el Centro Social de Oficiales de la Policía Nacional contemplando la posibilidad de optar por una migración hacia un sistema VoIP bajo software libre con Asterisk. En consecuencia, se deben evaluar las tecnologías actuales buscando proveer nuevas funcionalidades en el servicio telefónico generando bajos costos en su implementación, funcionamiento y mantenimiento.

Transportation Program Management System, HR-394, 2003

HR-394 was a software and database development project. Via funding provided by the Iowa Highway Research Board, the Iowa County Engineer's Association Service Bureau oversaw the planning and implementation of an Internet based application that supports two major local-government transportation project activities: Project programming and Development tracking. The goals were to reduce errors and inconsistencies, speed up the processes, link people to both project data and each other, and build a framework that could eventually support a 'paperless' work flow. The work started in 1999 and initial development was completed by the fall of 2002. Since going live, several 'piggy back' applications have been required to make the Programming side better fit actual work procedures. This part of the system has proven adequate but will be rewritten in 2004 to make it easier to use. The original development side module was rejected by the users and so had to be rewritten in 2003. The second version has proven much better, is heavily used, and is interconnected with Iowa DOT project data systems. Now that the system is in operation, it will be maintained and operated by the ICEA Service Bureau as an ongoing service function.

Diseño e implementación de la base de datos de un sistema centralizado de control del gasto público de los parlamentos europeos

Desarrollo de un proyecto software consistente en una base de datos para el control del gasto público de los parlamentos europeos.

Development of LRFD Procedures for Bridge Pile Foundations in Iowa, Volume I: An Electronic Database for PIle LOad Tests (PILOT), TR-573, 2011

For well over 100 years, the Working Stress Design (WSD) approach has been the traditional basis for geotechnical design with regard to settlements or failure conditions. However, considerable effort has been put forth over the past couple of decades in relation to the adoption of the Load and Resistance Factor Design (LRFD) approach into geotechnical design. With the goal of producing engineered designs with consistent levels of reliability, the Federal Highway Administration (FHWA) issued a policy memorandum on June 28, 2000, requiring all new bridges initiated after October 1, 2007, to be designed according to the LRFD approach. Likewise, regionally calibrated LRFD resistance factors were permitted by the American Association of State Highway and Transportation Officials (AASHTO) to improve the economy of bridge foundation elements. Thus, projects TR-573, TR-583 and TR-584 were undertaken by a research team at Iowa State University’s Bridge Engineering Center with the goal of developing resistance factors for pile design using available pile static load test data. To accomplish this goal, the available data were first analyzed for reliability and then placed in a newly designed relational database management system termed PIle LOad Tests (PILOT), to which this first volume of the final report for project TR-573 is dedicated. PILOT is an amalgamated, electronic source of information consisting of both static and dynamic data for pile load tests conducted in the State of Iowa. The database, which includes historical data on pile load tests dating back to 1966, is intended for use in the establishment of LRFD resistance factors for design and construction control of driven pile foundations in Iowa. Although a considerable amount of geotechnical and pile load test data is available in literature as well as in various State Department of Transportation files, PILOT is one of the first regional databases to be exclusively used in the development of LRFD resistance factors for the design and construction control of driven pile foundations. Currently providing an electronically organized assimilation of geotechnical and pile load test data for 274 piles of various types (e.g., steel H-shaped, timber, pipe, Monotube, and concrete), PILOT (http://srg.cce.iastate.edu/lrfd/) is on par with such familiar national databases used in the calibration of LRFD resistance factors for pile foundations as the FHWA’s Deep Foundation Load Test Database. By narrowing geographical boundaries while maintaining a high number of pile load tests, PILOT exemplifies a model for effective regional LRFD calibration procedures.

Pavement Thickness Design for Local Roads in Iowa, TR-586, 2010

Three pavement design software packages were compared with regards to how they were different in determining design input parameters and their influences on the pavement thickness. StreetPave designs the concrete pavement thickness based on the PCA method and the equivalent asphalt pavement thickness. The WinPAS software performs both concrete and asphalt pavements following the AASHTO 1993 design method. The APAI software designs asphalt pavements based on pre-mechanistic/empirical AASHTO methodology. First, the following four critical design input parameters were identified: traffic, subgrade strength, reliability, and design life. The sensitivity analysis of these four design input parameters were performed using three pavement design software packages to identify which input parameters require the most attention during pavement design. Based on the current pavement design procedures and sensitivity analysis results, a prototype pavement design and sensitivity analysis (PD&SA) software package was developed to retrieve the pavement thickness design value for a given condition and allow a user to perform a pavement design sensitivity analysis. The prototype PD&SA software is a computer program that stores pavement design results in database that is designed for the user to input design data from the variety of design programs and query design results for given conditions. The prototype Pavement Design and Sensitivity Analysis (PA&SA) software package was developed to demonstrate the concept of retrieving the pavement design results from the database for a design sensitivity analysis. This final report does not include the prototype software which will be validated and tested during the next phase.

Iowa Bridge Backwater Software User Manual, Version 2.0, TR-564, 2010

This manual describes how to use the Iowa Bridge Backwater software. It also documents the methods and equations used for the calculations. The main body describes how to use the software and the appendices cover technical aspects. The Bridge Backwater software performs 5 main tasks: Design Discharge Estimation; Stream Rating Curves; Floodway Encroachment; Bridge Backwater; and Bridge Scour. The intent of this program is to provide a simplified method for analysis of bridge backwater for rural structures located in areas with low flood damage potential. The software is written in Microsoft Visual Basic 6.0. It will run under Windows 95 or newer versions (i.e. Windows 98, NT, 2000, XP and later).

A database to aid the identification of chemicals potentially posing a health risk through percutaneous exposure

In the context of recent attempts to redefine the 'skin notation' concept, a position paper summarizing an international workshop on the topic stated that the skin notation should be a hazard indicator related to the degree of toxicity and the potential for transdermal exposure of a chemical. Within the framework of developing a web-based tool integrating this concept, we constructed a database of 7101 agents for which a percutaneous permeation constant can be estimated (using molecular weight and octanol-water partition constant), and for which at least one of the following toxicity indices could be retrieved: Inhalation occupational exposure limit (n=644), Oral lethal dose 50 (LD50, n=6708), cutaneous LD50 (n=1801), Oral no observed adverse effect level (NOAEL, n=1600), and cutaneous NOAEL (n=187). Data sources included the Registry of toxic effects of chemical substances (RTECS, MDL information systems, Inc.), PHYSPROP (Syracuse Research Corp.) and safety cards from the International Programme on Chemical Safety (IPCS). A hazard index, which corresponds to the product of exposure duration and skin surface exposed that would yield an internal dose equal to a toxic reference dose was calculated. This presentation provides a descriptive summary of the database, correlations between toxicity indices, and an example of how the web tool will help industrial hygienist decide on the possibility of a dermal risk using the hazard index.

Ingeniería del software: portal web inmobiliario

En la actualidad las tecnologías de la información son utilizadas en todos los ámbitos empresariales. Desde sistemas de gestión (ERPs) pasando por la gestión documental, el análisis de información con sistema de Bussines Intelligence, pudiendo incluso convertirse en toda una nueva plataforma para proveer a las empresas de nuevos canales de venta, como es el caso deInternet.De la necesidad inicial de nuestro cliente en comenzar a expandirse por un nuevo canal de venta para poder llegar a nuevos mercados y diversificar sus clientes se inicia la motivación de este TFC.Dadas las características actuales de las tecnologías de la información e internet, estas conforman un binomio perfecto para definir este TFC que trata todos los aspectos necesarios para llegar a obtener un producto final como es un portal web inmobiliario adaptado a los requisitos demandados por los usuarios actuales de Internet.

Suport i seguiment TFC Annapurna

El present treball exposa la planificació, disseny, anàlisi i arquitectura d'una aplicació creada amb tecnologia JEE. L'aplicació pretén ser una eina de suport psicològic a nens i nenes que tenen difícil accés a aquests professionals. La idea inicial es va inspirar en els infant d'un orfenat de Katmandú. Les tecnologies emprades per la realització del treball han estat Struts2, JSP i EJB3.0. Com a base de dades s'ha seleccionat MySQL, i el servidor d'aplicacions Jboss.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Las TIC como instrumento para la colaboración en red, en el ámbito de la Formación Profesional : Una aplicación de software libre para colaboración entre centro-empresa en el I.E.S El Palau

Investigaremos cómo las redes de colaboración y el softwarelibre permiten adaptar el centro educativo al entorno, cómo pueden ayudar al centro a potenciar la formación profesional y garantizar la durabilidad de las acciones, con el objetivo que perdure el conocimiento y la propia red de colaboración para una mejora educativa.

Implementación de GNU/Linux para una empresa de desarrollo de software

Trabajo que muestra, haciendo uso de tecnologías libres y basándonos en sistemas operativos abiertos, cómo es posible mantener un nivel alto de trabajo para una empresa que se dedica a implementar y realizar desarrollos en tecnologías de software libre. Se muestra el montaje de un laboratorio de desarrollo que nos va a permitir entender el funcionamiento y la implementación tanto de GNU/Linux como del software que se basa en él dentro de la infraestructura de la empresa.

Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information

Abstract

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

Software for interpolation of vegetative growth of yerba mate plants in 3D

The objective of this work was to build mock-ups of complete yerba mate plants in several stages of development, using the InterpolMate software, and to compute photosynthesis on the interpolated structure. The mock-ups of yerba-mate were first built in the VPlants software for three growth stages. Male and female plants grown in two contrasting environments (monoculture and forest understory) were considered. To model the dynamic 3D architecture of yerba-mate plants during the biennial growth interval between two subsequent prunings, data sets of branch development collected in 38 dates were used. The estimated values obtained from the mock-ups, including leaf photosynthesis and sexual dimorphism, are very close to those observed in the field. However, this similarity was limited to reconstructions that included growth units from original data sets. The modeling of growth dynamics enables the estimation of photosynthesis for the entire yerba mate plant, which is not easily measurable in the field. The InterpolMate software is efficient for building yerba mate mock-ups.