Techniques for storing and processing data are at the heart of all programs. The term data structure is used to describe the way data is stored, and the term algorithm is used to describe the way data is processed.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
Example to support XML files using MSXML.
=========================================
XML (Extensible Markup Language) is a commonly used basis for representing a
huge range of structured data file formats. The specification is maintained by
the World Wide Web Consortium (W3C?. This plugin is intended as an example of
how XML data can be read with a DataPlugin. You can adjust it to meet the needs
of your own XML file format.
After installing the URI file (double click on it), you can find the VBScript
file located at
"C:\Program Files\National Instruments\Shared\USI\plugins\DataPlugins\XML Example"
You can make changes to this file to read your specific XML file.
％
％ set some variables in the workspace to control behaviour:
％
％ graphicsMode 0 no graphics,
％ 1 graphics, particles, beacons, ground truth
％ 2 graphics, as above + range circles
％
％ perfect 0 use beacon range data
％ 1 use ground truth range data
％ clear a
Abstract
The Lucene Server project is an attempt to extend the Jakarta Lucene tool with server capabilities.
Lucene is a robust Java API that enables you creating indexes from text sources and perform powerful searches on these indexes. With Lucene, creating an index must be done programmatically and there are almost no possibilities of integrating index management in a distributed environment. In other words, out of the box, Lucene is suitable for integrating indexing and searching possibilities in a single application but not for providing index/search services for multiple applications.
The Lucene Server project comes with a Java API that propose the following
make it easy to create indexes in a declarative way by simply providing an XML configuration document.
make it easy to personalize the way Lucene must handle different kind of data sources.
provide services for index management and searching that can be accessed from several applications.
enable batch tasks scheduling.
today bought a book, reflected good to upload source code package. 1. Based on the struts of customer information management system 2. Struts-based personnel management system 3. Office log system 4. E-government management system 5. Food industry Invoicing System 6 SMS Data Acquisition System
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.
Returns weighted percentiles of a sample given the weight vector w
％ The idea is to give more emphasis in some examples of data as compared to
％ others by giving more weight. For example, we could give lower weights to
％ the outliers.
％ The motivation to write this function is to compute percentiles for Monte
％ Carlos simulations where some simulations are very bad (in terms of goodness
％ of fit between simulated and actual value) than the others and to give
％ the lower weights based on some goodness of fit criteria.
PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis practice. For the latter part it is assumed (but not
absolutely necessary) that the reader is already familiar with these
methods. It also assumes you have had some preliminary experience
with linear/matrix algebra.
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa.
For example,
>> project.name = MyProject
>> project.id = 1234
>> project.param.a = 3.1415
>> project.param.b = 42
becomes with str=xml_format(project, off )
"<project>
<name>MyProject</name>
<id>1234</id>
<param>
<a>3.1415</a>
<b>42</b>
</param>
</project>"
On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
