The LabVIEW Development Guidelines describe many of the issues that
arise when developing large applications. The guidelines are based on the
advice of LabVIEW developers, and provide a basic survey of software
engineering techniques you might find useful when developing your
own projects.
There is also a Discussion of style for creating VIs. Developers who have
used LabVIEW and are comfortable in the LabVIEW environment can use
the LabVIEW Development Guidelines to maintain a consistent and
effective style in their projects.
This cookbook contains a wealth of solutions to problems that SQL programmers face all the time. Recipes inside range from how to perform simple tasks, like importing external data, to ways of handling issues that are more complicated, like set algebra. Each recipe includes a Discussion that explains the logic and concepts underlying the solution. The book covers audit logging, hierarchies, importing data, sets, statistics, temporal data, and data structures.
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed Discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed Discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
PHP Cookbook has a wealth of solutions for problems that you ll face regularly. With topics that range from beginner questions to advanced web programming techniques, this guide contains practical examples -- or "recipes" -- for anyone who uses this scripting language to generate dynamic web content. Updated for PHP 5, this book provides solutions that explain how to use the new language features in detail, including the vastly improved object-oriented capabilities and the new PDO data access extension. New sections on classes and objects are included, along with new material on processing XML, building web services with PHP, and working with SOAP/REST architectures. With each recipe, the authors include a Discussion that explains the logic and concepts underlying the solution.
Digital Signal and Image Processing Using MATLAB
The most important theoretical aspects of image and signal processing (ISP) for both deterministic and random signals are covered in this guide to using MATLAB® . The Discussion is also supported by exercises and computer simulations relating to real applications such as speech processing and fetal-heart–rhythm tracking, and more than 200 programs and functions for numerical experiments are provided with commentary.
The Ruby Way takes a "how-to" approach to Ruby programming with the bulk of the material consisting of more than 400 examples arranged by topic. Each example answers the question "How do I do this in Ruby?" Working along with the author, you are presented with the task description and a Discussion of the technical constraints. This is followed by a step-by-step presentation of one good solution. Along the way, the author provides detailed commentary and explanations to aid your understanding.
Visual tracking is one of the key components for robots
to accomplish a given task in a dynamic environment,
especially when independently moving objects are included.
This paper proposes an extension of Adaptive
Visual Servoing (hereafter, AVS) for unknown moving
object tracking. The method utilizes binocular stereo
vision, but does not need the knowledge of camera parameters.
Only one assumption is that the system
need stationary references in the both images by which
the system can predict the motion of unknown moving
objects. The basic ideas how we extended the AVS
method such that it can track unknown moving objects
are given and formalized into a new AVS system. The
experimental results with proposed control architecture
are shown and a Discussion is given.
