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The Flightgear Manual
The FlightGear Manual Michael Basler, Martin Spott, Stuart Buchanan, Jon Berndt, Bernhard Buckel, Cameron Moore, Curt Olson, Dave Perry, Michael Selig, Darrell Walisser, and others The FlightGear Manual February 22, 2010 For FlightGear version 2.0.0 2 Contents 0.1 Condensed Reading.........................6 0.2 Instructions For the Truly Impatient................6 0.3 Further Reading...........................6 I Installation9 1 Want to have a free flight? Take FlightGear! 11 1.1 Yet Another Flight Simulator?................... 11 1.2 System Requirements........................ 14 1.3 Choosing A Version......................... 15 1.4 Flight Dynamics Models...................... 16 1.5 About This Guide.......................... 16 2 Preflight: Installing FlightGear 19 2.1 Installing scenery.......................... 19 2.1.1 MS Windows Vista/7.................... 20 2.1.2 Mac OS X......................... 20 2.1.3 FG_SCENERY....................... 20 2.1.4 Fetch Scenery as you fly.................. 21 2.1.5 Creating your own Scenery................. 22 2.2 Installing aircraft.......................... 22 2.3 Installing documentation...................... 22 II Flying with FlightGear 25 3 Takeoff: How to start the program 27 3.1 Environment Variables....................... 27 3.1.1 FG_ROOT......................... 27 3.1.2 FG_SCENERY....................... 27 3.1.3 Environment Variables on Windows and Mac OS X.... 27 3.2 Launching the simulator under Unix/Linux............ 28 3.3 Launching the simulator under Windows.............. 29 3.3.1 Launching from the command line............. 30 3 4 CONTENTS 3.4 Launching the simulator under Mac OS X............. 30 3.4.1 Selecting an aircraft and an airport............. 31 3.4.2 Enabling on-the-fly scenery downloading......... 31 3.4.3 Enabling Navigation Map (Atlas)............ -
Bioimagesuite Manual 95522 2
v2.6 c Copyright 2008 X. Papademetris, M. Jackowski, N. Rajeevan, R.T. Constable, and L.H Staib. Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine. All Rights Reserved ii Draft July 18, 2008 v2.6 Contents I A. Overview 1 1. Introduction 2 1.1. BioImage Suite Functionality . 3 1.2. BioImage Suite Software Infrastructure . 4 1.3. A Brief History . 4 2. Background 6 2.1. Applications of Medical Imaging Analysis: A Brief Overview . 6 2.2. Medical Image Processing & Analysis . 8 2.3. Software Development Related to Medical Image Analysis . 9 2.4. 3D Graphics and Volume Rendering . 11 3. Starting and Running BioImage Suite 18 3.1. Installation Overview . 18 3.2. Installation Instructions . 19 3.3. The Main BioImage Suite Menu . 22 3.4. Preferences Editor . 23 4. Application Structure 26 4.1. Application Structure Overview . 26 4.2. The File Menu . 27 4.3. The Display Menu . 31 5. Looking At Images 32 5.1. Image Formats . 32 5.2. The Viewers . 33 5.3. The Colormap Editor . 37 5.4. Coordinates for NeuroImaging . 39 5.5. Atlas Tools . 44 6. Advanced Image Visualization 47 6.1. 4D Images . 47 6.2. 3D Rendering Controls . 48 6.3. Volume Rendering . 48 6.4. Oblique Slices . 53 6.5. The Animation Tool . 54 iii Draft July 18, 2008 CONTENTS v2.6 II B. Anatomical Image Analysis 59 7. The Image Processing and Histogram Tools 60 7.1. Introduction . 60 7.2. “Image” and “Results” . 61 7.3. Histogram Control . 62 7.4. -
Database Programming with Python
Database programming with Python Magnus Lyckå Thinkware AB www.thinkware.se EuroPython Conference 2004 Chalmers, Göteborg, Sweden © 2004, Magnus Lyckå In the next 30 minutes you should... ● Learn about various database solutions for Python, and what they are good for. ● Get aquainted to the Python DB-API 2.0 and see some examples of how it is used. ● Get a brief introduction to an object oriented layer on top of the DB-API. ● Get a brief introduction to the Zope Object Database (ZODB). What's a database? ● “...a logically coherent collection of data with some inherent meaning” −Fundamentals of Database Systems ● “..an information set with a regular structure” – Wikipedia ● Persistent data storage ● Structure, consistency, relationships ● Transactions (ACID, 2PC) ● Backup/Restore/Replication/Security ● Ad hoc queries – not just for one program... What's it for? ● One user – Thousands of simultaneous users ● Handheld device – Cluster of servers ● Amusement – Life support system ● Mainly lookup – Lots of transactions ● Uniform data – Widely varied kinds of data ● Simple structures – Complex relationships ● etc... Types of database systems ● Deployment ● Interface between – Embedded in application program and database – Standalone server process system ● Networked / Distributed – Dictionary-like ● Database structure – Special API – – (Hierachical/Network) Special language (SQL) – – Simple table (flat) Seamless – Relational – Object-oriented Standard modules ● Unix style dbm – anydbm, dbhash (bsddb), gdbm, dbm, dumbdbm – Embedded – Standard -
Generic Pattern Mining Via Data Mining Template Library *
Generic Pattern Mining via Data Mining Template Library ⋆ Mohammed J. Zaki, Nilanjana De, Feng Gao, Paolo Palmerini ⋆⋆, Nagender Parimi, Jeevan Pathuri, Benjarath Phoophakdee, and Joe Urban Computer Science Department, Rensselaer Polytechnic Institute, Troy NY 12180 Abstract. Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collec- tion of generic containers and algorithms for data mining, as well as persistency and database management classes. DMTL provides a sys- tematic solution to a whole class of common FPM tasks like itemset, se- quence, tree and graph mining. DMTL is extensible, scalable, and high- performance for rapid response on massive datasets. A detailed set of experiments show that DMTL is competitive with special purpose al- gorithms designed for a particular pattern type, especially as database sizes increase. 1 Introduction Frequent Pattern Mining (FPM) is a very powerful paradigm which encompasses an entire class of data mining tasks, namely those dealing with extracting infor- mative and useful patterns in massive datasets representing complex interactions between diverse entities from a variety of sources. These interactions may also span multiple-scales, as well as spatial and temporal dimensions. FPM is ideally suited for categorical datasets, which include text/hypertext data (e.g., news ar- ticles, web pages), semistructured and XML data, event or log data (e.g., network logs, web logs), biological sequences (e.g. DNA/RNA, proteins), transactional datasets, and so on. FPM techniques are able to extract patterns embedded in different subspaces within very high dimensional, massive datasets. -
Tabla De Contenido
TABLA DE CONTENIDO INTRODUCCIÓN ............................................................................................................ 3 ALCANCE Y PRODUCTOS ........................................................................................... 4 OBJETIVOS ..................................................................................................................... 5 OBJETIVO GENERAL .................................................................................................... 5 OBJETIVOS ESPECÍFICOS ........................................................................................... 5 1. ESTADO DEL ARTE DE LAS BASES DE DATOS ORIENTADAS A OBJETOS EMBEBIDAS ................................................................................................................... 6 1.1. BASES DE DATOS ORIENTADAS A OBJETOS (BDOO) ............................... 6 1.1.1. Características generales de las Bases de Datos Orientadas a Objetos (BDOO)7 1.2. BASES DE DATOS OBJETO-RELACIONAL (BDOR) ................................... 11 1.2.2. Características generales de las BDOR ............................................................ 13 1.3. BASES DE DATOS EMBEBIDAS .................................................................... 13 1.3.1. Definiciones ..................................................................................................... 13 1.3.2. Características generales de las Bases de Datos Embebidas .......................... 14 1.3.3. Bases de Datos Embebidas Comerciales ........................................................ -
Free and Open Source Software
Free and open source software Copyleft ·Events and Awards ·Free software ·Free Software Definition ·Gratis versus General Libre ·List of free and open source software packages ·Open-source software Operating system AROS ·BSD ·Darwin ·FreeDOS ·GNU ·Haiku ·Inferno ·Linux ·Mach ·MINIX ·OpenSolaris ·Sym families bian ·Plan 9 ·ReactOS Eclipse ·Free Development Pascal ·GCC ·Java ·LLVM ·Lua ·NetBeans ·Open64 ·Perl ·PHP ·Python ·ROSE ·Ruby ·Tcl History GNU ·Haiku ·Linux ·Mozilla (Application Suite ·Firefox ·Thunderbird ) Apache Software Foundation ·Blender Foundation ·Eclipse Foundation ·freedesktop.org ·Free Software Foundation (Europe ·India ·Latin America ) ·FSMI ·GNOME Foundation ·GNU Project ·Google Code ·KDE e.V. ·Linux Organizations Foundation ·Mozilla Foundation ·Open Source Geospatial Foundation ·Open Source Initiative ·SourceForge ·Symbian Foundation ·Xiph.Org Foundation ·XMPP Standards Foundation ·X.Org Foundation Apache ·Artistic ·BSD ·GNU GPL ·GNU LGPL ·ISC ·MIT ·MPL ·Ms-PL/RL ·zlib ·FSF approved Licences licenses License standards Open Source Definition ·The Free Software Definition ·Debian Free Software Guidelines Binary blob ·Digital rights management ·Graphics hardware compatibility ·License proliferation ·Mozilla software rebranding ·Proprietary software ·SCO-Linux Challenges controversies ·Security ·Software patents ·Hardware restrictions ·Trusted Computing ·Viral license Alternative terms ·Community ·Linux distribution ·Forking ·Movement ·Microsoft Open Other topics Specification Promise ·Revolution OS ·Comparison with closed -
ZODB/ZEO Programming Guide Release 3.2.5
ZODB/ZEO Programming Guide Release 3.2.5 A.M. Kuchling January 13, 2005 [email protected] Contents 1 Introduction 2 1.1 What is the ZODB? ............................................ 2 1.2 OODBs vs. Relational DBs ........................................ 2 1.3 What is ZEO? ............................................... 4 1.4 About this guide .............................................. 4 1.5 Acknowledgements ............................................ 4 2 ZODB Programming 5 2.1 Installing ZODB ............................................. 5 Requirements ............................................... 5 Installing the Packages .......................................... 5 2.2 How ZODB Works ............................................ 5 2.3 Opening a ZODB ............................................. 6 2.4 Writing a Persistent Class ......................................... 6 2.5 Rules for Writing Persistent Classes ................................... 8 Modifying Mutable Objects ........................................ 8 Some Special Methods Don’t Work ................................... 9 getattr , delattr , and setattr ........................ 9 2.6 Writing Persistent Classes ........................................ 10 Changing Instance Attributes ....................................... 10 3 ZEO 10 3.1 How ZEO Works ............................................. 10 3.2 Installing ZEO .............................................. 11 Requirements ............................................... 11 Running a server ............................................ -
A Reboot of the Starpack Build Process for Tcl 8.6 and Beyond
A Reboot of the Starpack Build Process for Tcl 8.6 and Beyond Stephen Huntley, Güneş Koru, Yili Zhang, Urmita Banerjee, Leroy Kim, Clif Flynt Health IT Lab at UMBC Department of Information Systems University of Maryland, Baltimore County Baltimore, Maryland, USA, 21250 [email protected], {gkoru,yili.zhang,urmita.banerjee,leroy.kim}@umbc.edu, [email protected] Abstract We describe an initiative to simplify drastically the compilation of Starkit basekits for Unix and Windows, which excises no-longer-needed code and relies wholly on Tcl tools for build scripts. Basekit code providing once- essential features is now redundant thanks to Tcl 8.6's feature set, and is eliminated. Our changes facilitate a professional standard of security auditing and accountability of basekit contents, make it easier to upgrade separate library components, and will ease future support of new platforms. Tcllib's TEPAM package was used to create a human-friendly command line wrapper, and Ghostscript's tmake.tcl (a partial clone of standard make) was used as a flexible and configurable backend compile engine. 1. Introduction But obtaining an appropriate basekit has in recent years become the most challenging step in an Starkits and Starpacks remain popular among Tcl otherwise compellingly simple process. In the developers and their clients as a simple and past, developers who did not wish to compile effective technology for delivering and deploying their own found it easy to rely on pre-compiled Tcl/Tk applications as a single file, within which basekits for a range of computer platforms, all code files, resources and libraries are stored as provided by trusted sources. -
Designtheory.Org
THE REPRESENTATION AND STORAGE OF COMBINATORIAL BLOCK DESIGNS by Hatem Nassrat Submitted in partial fulfillment of the requirements for the degree of Master of Applied Computer Science at Dalhousie University Halifax, Nova Scotia APRIL 2009 c Copyright by Hatem Nassrat, 2009 DALHOUSIE UNIVERSITY FACULTY OF COMPUTER SCIENCE The undersigned hereby certify that they have read and recommend to the Faculty of Graduate Studies for acceptance a thesis entitled “THE REPRESENTATION AND STORAGE OF COMBINATORIAL BLOCK DESIGNS” by Hatem Nassrat in partial fulfillment of the requirements for the degree of Master of Applied Computer Science. Dated: APRIL 15, 2009 Supervisors: P. Bodorik P. Dobcs´anyi Reader: C. Watters ii DALHOUSIE UNIVERSITY DATE: APRIL 15, 2009 AUTHOR: Hatem Nassrat TITLE: THE REPRESENTATION AND STORAGE OF COMBINATORIAL BLOCK DESIGNS DEPARTMENT OR SCHOOL: Faculty of Computer Science DEGREE: M.A.C.Sc. CONVOCATION: MAY YEAR: 2009 Permission is herewith granted to Dalhousie University to circulate and to have copied for non-commercial purposes, at its discretion, the above title upon the request of individuals or institutions. Signature of Author The author reserves other publication rights, and neither the thesis nor extensive extracts from it may be printed or otherwise reproduced without the author’s written permission. The author attests that permission has been obtained for the use of any copyrighted material appearing in the thesis (other than brief excerpts requiring only proper acknowledgement in scholarly writing) and that all such use is clearly acknowledged. iii Table of Contents Abstract ...................................... vii Chapter 1 Introduction .......................... 1 1.1 Combinatorial Block Designs ....................... 1 1.2 Project Phases: Motivation, Contributions, and Outline ....... -
Sqlite and Tcl Overview
SQLite and Tcl Overview SQLite is an SQL database engine. There are dozens of similar packages, both free and commercial. SQLite happens to be among the fastest and most compact. But there are also qualitative differences with SQLite that make it especially well suited for use in standalone Tcl and Tcl/Tk based software. If you have not used SQLite in your Tcl or Tcl/Tk applications before, you should give it another look. Features Of SQLite SQLite is a serverless database engine. Most other SQL database engines use a separate server process to manage data on the disk. Client programs employ interprocess communication (usually hidden behind a client-side library) to send SQL statements to the server and to receive back results. With SQLite, there is no server. Client programs talk directly to the database file using ordinary disk I/O operations. Because there is no separate server process to install, set up, and administer, SQLite is a zero-configuration database engine. A program that uses SQLite does not require a database administrator (DBA) to create a database instance, set up access permissions, assign passwords, allocate disk space, and so forth. SQLite just needs to know the name of the single ordinary disk file that holds its database. SQLite understands most of SQL-92. You can create tables and indices, of course. SQLite also support row triggers on tables and INSTEAD OF triggers on views. There are no arbitrary limits on the number of tables, indices, views, or triggers, nor on the number of columns in a table or index. -
Secure Content Distribution Using Untrusted Servers Kevin Fu
Secure content distribution using untrusted servers Kevin Fu MIT Computer Science and Artificial Intelligence Lab in collaboration with M. Frans Kaashoek (MIT), Mahesh Kallahalla (DoCoMo Labs), Seny Kamara (JHU), Yoshi Kohno (UCSD), David Mazières (NYU), Raj Rajagopalan (HP Labs), Ron Rivest (MIT), Ram Swaminathan (HP Labs) For Peter Szolovits slide #1 January-April 2005 How do we distribute content? For Peter Szolovits slide #2 January-April 2005 We pay services For Peter Szolovits slide #3 January-April 2005 We coerce friends For Peter Szolovits slide #4 January-April 2005 We coerce friends For Peter Szolovits slide #4 January-April 2005 We enlist volunteers For Peter Szolovits slide #5 January-April 2005 Fast content distribution, so what’s left? • Clients want ◦ Authenticated content ◦ Example: software updates, virus scanners • Publishers want ◦ Access control ◦ Example: online newspapers But what if • Servers are untrusted • Malicious parties control the network For Peter Szolovits slide #6 January-April 2005 Taxonomy of content Content Many-writer Single-writer General purpose file systems Many-reader Single-reader Content distribution Personal storage Public Private For Peter Szolovits slide #7 January-April 2005 Framework • Publishers write➜ content, manage keys • Clients read/verify➜ content, trust publisher • Untrusted servers replicate➜ content • File system protects➜ data and metadata For Peter Szolovits slide #8 January-April 2005 Contributions • Authenticated content distribution SFSRO➜ ◦ Self-certifying File System Read-Only -
Metakit: Quick and Easy Storage for Your Tcl Application1
DRAFT, February 22, 2002 MetaKit: Quick and Easy Storage for your Tcl Application1 Mark Roseman [email protected] Ever needed a simple and powerful way to store and retrieve data in your latest Tcl masterpiece? Finding yourself cursing at the explosion of small data files littering your disk as your application grew? Would you like to get a handle on efficiently extracting the right results from your data, without having to digest a 5-pound book on SQL to do it? Need storage that won’t require a painful database installation, a dedicated database analyst to maintain it, and all the associated Tcl extensions needed to get it to work with your code? If you’re like most developers, you probably would have answered “YES!!!” to these questions on several different occasions. If so, you could probably benefit from MetaKit, a storage engine that’s ideally matched to Tcl: dynamic, flexible, easy to learn, embeddable, and very powerful. In this document, we’ll explore how you can use MetaKit to solve the data storage needs of your Tcl applications. You’ll find out what makes MetaKit different than other approaches, and whether it’s the right approach for you. You’ll see how to use MetaKit from within your own Tcl programs, and learn the small number of MetaKit commands that you’ll need to use for most applications. In no time, you’ll be proficient enough that you’ll never (well, ok, hardly ever) look at a flat file storage solution again. So lets start by taking a closer look at what MetaKit provides you with.