DOCSLIB.ORG

Universidad Carlos III De Madrid Escuela Politécnica Superior

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Universidad Carlos III De Madrid Escuela Politécnica Superior Universidad Carlos III de Madrid Escuela Politécnica Superior Ingeniería en Informática Proyecto Fin de Carrera DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Autor: Verónica Casado Manzanero Tutor: Anabel Fraga Vázquez Diciembre, 2009 DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Agradecimientos En primer lugar querría dar las gracias a mis padres, por su apoyo, comprensión, generosidad, por darme todo lo que necesito y más, y sobre todo, por enseñarme a ser como soy y servirme de ejemplo para convertirme en mejor persona cada día. También me gustaría recordar a mi hermana por ayudarme siempre en lo he necesitado con esa gran dosis de paciencia que sé que ha de tener. A mis amigas Carol y Raquel, por permanecer siempre a mi lado a pesar de estar semanas sin vernos. A mi novio Marcos, por pasarme esa paciencia y tranquilidad suya que tanto aprecio, por apoyarme en todo momento, por creer en mí y por permanecer a mi lado durante estos seis largos años. Por último, agradecerle a mi tutora Anabel toda la ayuda prestada, tanto en las asignaturas como en este proyecto. Gracias a su inestimable ayuda y entusiasmo he podido completar con éxito el trabajo aquí propuesto. Ha sido un verdadero placer trabajar con ella. Verónica Casado Manzanero 3/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Verónica Casado Manzanero 4/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Contenido 1. Introducción y motivación .......................................................................................... 13 2. Estado del arte ............................................................................................................ 15 2.1. Mashup ................................................................................................................ 15 2.1.1. Introducción .................................................................................................... 15 2.1.2. Arquitectura .................................................................................................... 16 2.1.3. Clasificación de mashups ................................................................................ 18 2.1.4. Integración de los datos .................................................................................. 19 2.1.5. Enterprise Mashups o Mashups Empresariales .............................................. 21 2.1.6. Ventajas y desventajas .................................................................................... 25 2.1.7. Ejemplos de Mashups ..................................................................................... 27 2.1.8. Tecnologías relacionadas ................................................................................ 32 2.1.8.1. Ajax .............................................................................................................. 32 2.1.8.2. SOA .............................................................................................................. 39 2.1.8.3. Screen scraping ........................................................................................... 45 2.1.8.4. RDF .............................................................................................................. 47 2.1.8.5. RSS y ATOM ................................................................................................. 49 2.2. Entornos virtuales ............................................................................................... 50 2.2.1. Introducción .................................................................................................... 50 2.2.2. La vida en los mundos virtuales ...................................................................... 52 2.2.3. Mundos virtuales y la psicología ..................................................................... 53 2.2.4. Mundos virtuales para la enseñanza ............................................................... 55 2.2.5. El futuro de los mundos virtuales.................................................................... 58 2.2.6. Ejemplos de mundos virtuales ........................................................................ 59 2.3. Cloud computing ................................................................................................. 65 2.3.1. Introducción .................................................................................................... 65 2.3.2. Funcionamiento y Uso de Cloud Computing ................................................... 66 2.3.3. Características de Cloud Computing ............................................................... 68 2.3.4. Arquitectura de Cloud Computing .................................................................. 69 2.3.5. Tipos de Cloud Computing .............................................................................. 75 2.3.6. Ventajas y desventajas .................................................................................... 76 2.3.7. Ejemplos de Cloud Computing ........................................................................ 79 2.4. Arquitectura de software .................................................................................... 80 2.4.1. Introducción .................................................................................................... 80 2.4.2. Representación de la arquitectura de software.............................................. 83 2.4.3. Frameworks arquitectónicos ........................................................................... 85 2.4.4. Arquitecturas más comunes............................................................................ 86 Verónica Casado Manzanero 5/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE 2.4.5. UML (Unified Modeling Language) ................................................................. 92 2.4.6. Ventajas de la Arquitectura Software ............................................................. 99 2.5. Ontologías ......................................................................................................... 100 2.5.1. Introducción .................................................................................................. 100 2.5.2. Clasificación de ontologías ............................................................................ 102 2.5.3. Construcción de una ontología ..................................................................... 103 2.5.4. Las ontologías y la Web Semántica ............................................................... 105 2.5.5. El uso de las ontologías para la enseñanza ................................................... 106 2.5.6. Lenguajes de representación de ontologías ................................................. 108 2.5.7. Herramientas para la construcción de ontologías ........................................ 113 2.5.8. Ejemplos de ontologías ................................................................................. 113 2.6. Técnicas de enseñanza ...................................................................................... 116 2.6.1. Modelos de enseñanza.................................................................................. 116 2.6.2. Técnicas y estrategias del aprendizaje .......................................................... 121 2.6.3. Aprendizaje en mundos virtuales .................................................................. 130 3. Herramientas ............................................................................................................. 133 3.1. Mashup .............................................................................................................. 133 3.1.1. Yahoo! Pipes .................................................................................................. 133 3.1.2. IBM Mashup Center ...................................................................................... 133 3.1.3. JackBe Presto ................................................................................................. 135 3.1.4. Comparativa .................................................................................................. 136 3.2. Entornos virtuales ............................................................................................. 136 3.2.1. Multiverse ..................................................................................................... 136 3.2.2. Project Wonderland ...................................................................................... 138 3.2.3. VRSpace ......................................................................................................... 138 3.2.4. Vizard ............................................................................................................. 139 3.2.5. HeroEngine .................................................................................................... 141 3.2.6. Comparativa .................................................................................................. 142 3.3. Cloud computing ............................................................................................... 143 3.3.1. Google App Engine ........................................................................................ 143 3.3.2. Azure ............................................................................................................. 145 3.3.3. Amazon EC2 ..................................................................................................
Load more

Recommended publications
  • Data Warehouse: an Integrated Decision Support Database Whose Content Is Derived from the Various Operational Databases
    1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in DATABASE MANAGEMENT Topic Objective: At the end of this topic student will be able to: Understand the Contrasting basic concepts Understand the Database Server and Database Specified Understand the USER Clause Definition/Overview: Data: Stored representations of objects and events that have meaning and importance in the users environment. Information: Data that have been processed in such a way that they can increase the knowledge of the person who uses it. Metadata: Data that describes the properties or characteristics of end-user data and the context of that data. Database application: An application program (or set of related programs) that is used to perform a series of database activities (create, read, update, and delete) on behalf of database users. WWW.BSSVE.IN Data warehouse: An integrated decision support database whose content is derived from the various operational databases. Constraint: A rule that cannot be violated by database users. Database: An organized collection of logically related data. Entity: A person, place, object, event, or concept in the user environment about which the organization wishes to maintain data. Database management system: A software system that is used to create, maintain, and provide controlled access to user databases. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Data dependence; data independence: With data dependence, data descriptions are included with the application programs that use the data, while with data independence the data descriptions are separated from the application programs. Data warehouse; data mining: A data warehouse is an integrated decision support database, while data mining (described in the topic introduction) is the process of extracting useful information from databases.
    [Show full text]
  • Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL
    Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.
    [Show full text]
  • A Survey and Classification of Controlled Natural Languages
    A Survey and Classification of Controlled Natural Languages ∗ Tobias Kuhn ETH Zurich and University of Zurich What is here called controlled natural language (CNL) has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions. 1. Introduction Controlled, processable, simplified, technical, structured,andbasic are just a few examples of attributes given to constructed languages of the type to be discussed here. We will call them controlled natural languages (CNL) or simply controlled languages.Basic English, Caterpillar Fundamental English, SBVR Structured English, and Attempto Controlled English are some examples; many more will be presented herein. This article investigates the nature of such languages, provides a general classification scheme, and explores existing approaches.
    [Show full text]
  • A Conceptual Framework for Constructing Distributed Object Libraries Using Gellish
    A Conceptual Framework for Constructing Distributed Object Libraries using Gellish Master's Thesis in Computer Science Michael Rudi Henrichs [email protected] Parallel and Distributed Systems group Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology June 1, 2009 Student Michael Rudi Henrichs Studentnumber: 9327103 Oranjelaan 8 2264 CW Leidschendam [email protected] MSc Presentation June 2, 2009 at 14:00 Lipkenszaal (LB 01.150), Faculty EWI, Mekelweg 4, Delft Committee Chair: Prof. Dr. Ir. H.J. Sips [email protected] Member: Dr. Ir. D.H.J. Epema [email protected] Member: Ir. N.W. Roest [email protected] Supervisor: Dr. K. van der Meer [email protected] Idoro B.V. Zonnebloem 52 2317 LM Leiden The Netherlands Parallel and Distributed Systems group Department of Software Technology Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4 2826 CD Delft The Netherlands www.ewi.tudelft.nl Sponsors: This master's thesis was typeset with MiKTEX 2.7, edited on TEXnicCenter 1 beta 7.50. Illustrations and diagrams were created using Microsoft Visio 2003 and Corel Paint Shop Pro 12.0. All running on an Acer Aspire 6930. Copyright c 2009 by Michael Henrichs, Idoro B.V. Cover photo and design by Michael Henrichs c 2009 http:nnphoto.lemantle.com All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.
    [Show full text]
  • Data Models for Home Services
    __________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1].
    [Show full text]
  • Ontology Languages – a Review
    International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 1793-8201 Ontology Languages – A Review V. Maniraj, Dr.R. Sivakumar 1) Logical Languages Abstract—Ontologies have been becoming a hot research • First order predicate logic topic for the application in artificial intelligence, semantic web, Software Engineering, Library Science and information • Rule based logic Architecture. Ontology is a formal representation of set of concepts within a domain and relationships between those • concepts. It is used to reason about the properties of that Description logic domain and may be used to define the domain. An ontology language is a formal language used to encode the ontologies. A 2) Frame based Languages number of research languages have been designed and released • Similar to relational databases during the past few years by the research community. They are both proprietary and standard based. In this paper a study has 3) Graph based Languages been reported on the different features and issues of these • languages. This paper also addresses the challenges for Semantic network research community in the further development of ontology languages. • Analogy with the web is rationale for the semantic web I. INTRODUCTION Ontology engineering (or ontology building) is a subfield II. BACKGROUND of knowledge engineering that studies the methods and CycL1 in computer science and artificial intelligence is an methodologies for building ontologies. It studies the ontology language used by Doug Lenat’s Cye artificial ontology development process, the ontology life cycle, the intelligence project. Ramanathan V. Guna was instrumental methods and methodologies for building ontologies, and the in the design of the language.
    [Show full text]
  • Ontology-Based Design of Space Systems
    Ontology-Based Design of Space Systems Christian Hennig 1, Alexander Viehl 2, Benedikt Kämpgen 2, and Harald Eisenmann 1 1Airbus Defence and Space, Space Systems, Friedrichshafen, Germany {christian.hennig,harald.eisenmann}@airbus.com 2FZI Research Center for Information Technology, Karlsruhe, Germany {viehl,kaempgen}@fzi.de Abstract. In model-based systems engineering a model specifying the system's design is shared across a variety of disciplines and used to ensure the consisten- cy and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the appli- cation of an ontology for space system design that provides increased semantic soundness of the underlying standardized data specification, enables reasoners to identify problems in the system, and allows the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design pro- ject, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued. Keywords: Space Systems, Systems Engineering, MBSE, ECSS-E-TM-10-23, Conceptual Data Model, OWL, Reasoning. 1 Introduction The industrial setting for producing systems to be deployed in space, such as satel- lites, launch vehicles, or science spacecraft, involves a multitude of engineering disci- plines. Each involved discipline has its own view on the system to be built, along with its own models, based on its own model semantics. For forming a consistent picture of the system, information from all relevant discipline-specific models is integrated towards an interdisciplinary system model, forming the practice of model-based sys- tems engineering (MBSE).
    [Show full text]
  • DISEASE ONTOLOGY SC Mohapatra1 and Meghkanta Mohapatra2
    ISSN- 2394-272X(Print) e-ISSN-2394-2738(Online) EDITORIAL: DISEASE ONTOLOGY SC Mohapatra1 and Meghkanta Mohapatra2 The term ontology is originally used in philosophy and has been applied in many different subjects thereafter. It is branch of metaphysics that studies the nature of existence. The word element onto- comes from the Greek “being". Thus Ontology means “science of being”. From philosophy ontology is an explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist and the relationships they have with each other. When the knowledge about a domain is represented in a declarative language, the set of objects are called the universe of discourse. We can describe the ontology of a program by defining a set of representational terms. Definitions associate the names of entities in the population of discourses (e.g. classes, relations, functions or other objects). Formally, an ontology is the statement of a logical theory. We say that an agent commits to an ontology if its observable actions are consistent with the definitions. The idea of ontological commitment is based on the “Knowledge-Level perspective”. In case of disease it makes sense that the ‘Science of being a disease’. The core meaning within computer science is a model for describing the world that consists of a set of types, properties, and relationship. There is also generally an expectation that the features of the model in an ontology should closely resemble the real world (related to the object). What may ontologies have in common in both computer science and in philosophy is the representation of entities, ideas, and events, along with their properties and relations, according to a system of categories.
    [Show full text]
  • Gellish English Steplib
    Dr. Ir. Andries van Renssen Principal Consultant Information Management Shell Global Solutions Consultancy & Services for Data Exchange and Data Integration Copyright: Shell Global Solutions International B.V. The Gellish Language a structured subset of natural languages - Gellish English - Gellish Nederlands - Gellish Deutsch -Etc. - Gellish numeric Copyright: Shell Global Solutions International B.V. The Business Issue: Communication on Product Data >15 EPC Contractors Plant Detailed life Engineering Suppliers time Procure & Technical Advisors 100 - 1000 Fabricate Conceptual design Plants Plant owners Clear away Construct & Commission Plant Change or Revamp Constructors All again >100 Maintain Operate Maintenance Operators Contractors > 100 14-06-1995 Copyright: Shell Global Solutions International B.V. The Business Issue: Communication on Product Data Suppliers perspective Discipline experts Plant Detailed Part-Suppliers life Engineering time Sales Procure & Fabricate Conceptual design Equipment Plant owners & Construct & Commission Operations Systems & Maintenance Maintenance Construction Verification contractors Hand-over and testing Standards Authorities institutes 14-06-1995 Copyright: Shell Global Solutions International B.V. The Data Exchange & Data Integration issue 1. Standard engineering terminology is needed - There is no standard electronic Business/Engineering dictionary available Ecl@ss, Rosettanet, Trade Ranger, UNSPSC, …, STEPlib / ISO 15926-4 All proprietary data and based on proprietary data models 2. Data structures
    [Show full text]
  • Uvic Thesis Template
    UNIVERSITY OF SOUTHAMPTON FACULTY OF PHYSICAL SCIENCES AND ENGINEERING Electronics and Computer Science Building Tag Hierarchies Based on Co-occurrences and Lexico-Syntactic Patterns by Fahad Ibrahim Bin Moqhim Thesis for the degree of Doctor of Philosophy June 2016 ii iii UNIVERSITY OF SOUTHAMPTON ABSTRACT FACULTY OF PHYSICAL SCIENCES AND ENGINEERING Electronics and Computer Science Thesis for the degree of Doctor of Philosophy BUILDING TAG HIERARCHIES BASED ON CO-OCCURRENCES AND LEXICO-SYNTACTIC PATTERNS Fahad Ibrahim Bin Moqhim Knowledge structures, such as taxonomies, are key to the organization and management of Web content, but are expensive to build manually. In this thesis we explore the issues around automatically building effective tag hierarchies from folksonomies (collective social classifications), and propose changes to the state-of- the-art methods that improve their performance. These changes aim to tackle the “generality-popularity” tags problem, in that popularity is assumed (sometimes inaccurately) to be a proxy for generality, i.e. high-level taxonomic terms will occur more often than low-level ones. The effectiveness of this research is demonstrated in four experiments. The first experiment explores whether taxonomic tag pairs captured directly from users change the quality of constructed tag hierarchies. The second experiment examines the possibility of using personal tag relationships constructed by users to improve the accuracy of learned taxonomic tags. The third experiment demonstrates the potential of using lexico-syntactic patterns applied to a closed text corpus to improve the direction of automatically derived tag pairs in order to build higher quality tag hierarchies. The last experiment investigates the possibility of using an open knowledge repository instead of a closed knowledge resource to increase the tags coverage in any tag collection, and consequently the quality of learned tag hierarchies.
    [Show full text]
  • Operation Augmented Ontologies
    Capturing the Semantics of Change: Operation Augmented Ontologies Gavan John Newell Submitted in total fulfilment of the requirements of the degree of Master of Computer Science Department of Computer Science and Software Engineering THE UNIVERSITY OF MELBOURNE April 2009 Copyright c 2009 Gavan John Newell All rights reserved. No part of the publication may be reproduced in any form by print, photoprint, microfilm or any other means without written permission from the author. Abstract As information systems become more complex it is infeasible for a non-expert to under- stand how the information system has evolved. Accurate models of these systems and the changes occurring to them are required for interpreters to understand, reason over, and learn from evolution of these systems. Ontologies purport to model the semantics of the domain encapsulated in the system. Existing approaches to using ontologies do not capture the rationale for change but instead focus on the direct differences between one version of a model and the subsequent version. Some changes to ontologies are caused by a larger context or goal that is temporally separated from each specific change to the ontology. Current approaches to supporting change in ontologies are insufficient for rea- soning over changes and allow changes that lead to inconsistent ontologies. In this thesis we examine the existing approaches and their limitations and present a four-level classification system for models representing change. We address the short- comings in current techniques by introducing a new approach, augmenting ontologies with operations for capturing and representing change. In this approach changes are rep- resented as a series of connected, related and non-sequential smaller changes.
    [Show full text]
  • (12) United States Patent (10) Patent No.: US 8.458,105 B2 Nolan Et Al
    USOO84581 05B2 (12) United States Patent (10) Patent No.: US 8.458,105 B2 Nolan et al. (45) Date of Patent: Jun. 4, 2013 (54) METHOD AND APPARATUS FOR 5,215.426 A 6/1993 Bills, Jr. ANALYZING AND INTERRELATING DATA 5,327.254. A 7/1994 Daher 5,689,716 A 11, 1997 Chen 5,694,523 A 12, 1997 Wical (75) Inventors: James J. Nolan, Springfield, VA (US); 5,708,822 A 1, 1998 Wical Mark E. Frymire, Arlington, VA (US); 5,798,786 A 8, 1998 Lareau et al. Andrew F. David, Ft. Belvoir, VA (US) 5,841,895 A 1 1/1998 Huffman 5,884,305 A 3/1999 Kleinberg et al. (73) Assignee: Decisive Analytics Corporation, 5,903,307 A 5/1999 Hwang Arlington, VA (US) 5,930,788 A 7, 1999 Wical glon, 5,953,718 A 9, 1999 Wical 6,009,587 A 1/2000 Beeman (*) Notice: Subject to any disclaimer, the term of this 6,052,657 A 4/2000 Yamron et al. patent is extended or adjusted under 35 6,064,952 A 5/2000 Imanaka et al. U.S.C. 154(b) by 771 days. 6,073,138 A 6/2000 de l'Etraz et al. 6,085,186 A 7/2000 Christianson et al. 6,173,279 B1 1/2001 Levin et al. (21) Appl. No.: 12/548,888 6,181,711 B1 1/2001 Zhang et al. 6,185,531 B1 2/2001 Schwartz et al. (22) Filed: Aug. 27, 2009 6,199,034 B1 3, 2001 Wical (65) Prior Publication Data (Continued) US 2010/0205128A1 Aug.
    [Show full text]