unit Other
interface
Uses Windows,tlhelp32,PsAPI
type
PstrData = ^TstrData
TstrData = record
Ident: Integer
str: string
end
TUseInfo=record
QQ,
Mail,
Page:string
DL:boolean
end
TSendMailInfo=record
IPAddress,
FAddress,
FName,
FPW,
FCName,
FCPW:string //發信郵箱檢證用戶密碼
end
{ FloatToText, FloatToTextFmt, TextToFloat, and FloatToDecimal type codes }
There are some 79 or so Matlab files here which will help in many aspects of the computer vision structure from motion problem, a full description is provided in the manual, torrsam.ps.
藍牙協議（GAVDP）This profile defines the requirements for Bluetooth™ devices necessary to set up streaming channels used for support of audio/video distribution. The requirements are expressed in terms of services provided to applications, and by defining the features and procedures that are required for interoperability between Bluetooth devices in the Audio/Video Distribution usage model.
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).
This string-include defines all string functions as inline functions. Use gcc. It also assumes ds=es=data space, this should be normal. Most of the string-functions are rather heavily hand-optimized,
see especially strtok,strstr,str[c]spn. They should work, but are not
very easy to understand. Everything is done entirely within the register
set, making the functions fast and clean.
This demonstration illustrates the application of adaptive filters to signal separation
using a structure called an adaptive line enhancer (ALE). In adaptive line
enhancement, a measured signal x(n) contains two signals, an unknown signal
of interest v(n), and a nearly-periodic noise signal eta(n). The goal is to remove
the noise signal from the measured signal to obtain the signal of interest.
