Windowed-Burg method is made in order to improve the Clasical Burg method. previously, I send the PBURGW.m file, but now I include also the ARBURGW.m algorithm and some NOTES-EXAMPLES to explain it and compare with the pburg.m algorithm from MATLAB.
The zlibex.pas unit included in this archive will work with delphi 5, 6,and 7. if you previously downloaded my delphi 5 unit, you will notice that the unit has been renamed. this was done because borland included in its delphi 6 and 7 lib directories a zlib.dcu file and i felt it was more correct to rename my unit and force developers to have to update their code than to make developers worry about the possible file contention in delphi 6 and 7.
I. Introduction
This code exploits a previously undisclosed vulnerability in the bit string
decoding code in the Microsoft ASN.1 library. This vulnerability is not related
to the bit string vulnerability described in eEye advisory AD20040210-2. Both
vulnerabilities were fixed in the MS04-007 patch.
II. Screenshots
$ ./kill-bill.pl
. kill-bill : Microsoft ASN.1 remote exploit for CAN-2003-0818 (MS04-007)
by Solar Eclipse <solareclipse@phreedom.org>
Usage: kill-bill -p <port> -s <service> host
Services:
iis IIS HTTP server (port 80)
iis-ssl IIS HTTP server with SSL (port 443)
exchange Microsoft Exchange SMTP server (port 25)
smb-nbt SMB over NetBIOS (port 139)
smb SMB (port 445)
If a service is running on its default port you don t have to
specify both the service and the port.
Examples: kill-bill -s iis 192.168.0.1
kill-bill -p 80 192.168.0.1
kill-bill -p 1234 -s smb 192.168.0.1
Abstract—Wireless networks in combination with image
sensors open up a multitude of previously unthinkable sensing
applications. Capable tools and testbeds for these wireless image
sensor networks can greatly accelerate development of complex,
yet efficient algorithms that meet application requirements. In this
paper, we introduce WiSNAP, a Matlab-based application
development platform intended for wireless image sensor
networks. It allows researchers and developers of such networks
to investigate, design, and evaluate algorithms and applications
using real target hardware. WiSNAP offers standardized and
easy-to-use Application Program Interfaces (APIs) to control
image sensors and wireless motes, which do not require detailed
knowledge of the target hardware. Nonetheless, its open system
architecture enables support of virtually any kind of sensor or
wireless mote. Application examples are presented to illustrate the
usage of WiSNAP as a powerful development tool.
There has long been a need for portable ultrasoundsystems that have good resolution at affordable costpoints. Portable systems enable healthcare providersto use ultrasound in remote locations such asdisaster zones, developing regions, and battlefields,where it was not previously practical to do so.
Design techniques for electronic systems areconstantly changing. In industries at the heart of thedigital revolution, this change is especially acute.Functional integration, dramatic increases incomplexity, new standards and protocols, costconstraints, and increased time-to-market pressureshave bolstered both the design challenges and theopportunities to develop modern electronic systems.One trend driving these changes is the increasedintegration of core logic with previously discretefunctions to achieve higher performance and morecompact board designs.
There has long been a need for portable ultrasoundsystems that have good resolution at affordable costpoints. Portable systems enable healthcare providersto use ultrasound in remote locations such asdisaster zones, developing regions, and battlefields,where it was not previously practical to do so.
Design techniques for electronic systems areconstantly changing. In industries at the heart of thedigital revolution, this change is especially acute.Functional integration, dramatic increases incomplexity, new standards and protocols, costconstraints, and increased time-to-market pressureshave bolstered both the design challenges and theopportunities to develop modern electronic systems.One trend driving these changes is the increasedintegration of core logic with previously discretefunctions to achieve higher performance and morecompact board designs.
This paper presents an interactive technique that
produces static hairstyles by generating individual hair strands
of the desired shape and color, subject to the presence of gravity
and collisions. A variety of hairstyles can be generated by
adjusting the wisp parameters, while the deformation is solved
efficiently, accounting for the effects of gravity and collisions.
Wisps are generated employing statistical approaches. As for
hair deformation, we propose a method which is based on
physical simulation concepts but is simplified to efficiently
solve the static shape of hair. On top of the statistical wisp
model and the deformation solver, a constraint-based styler
is proposed to model artificial features that oppose the natural
flow of hair under gravity and hair elasticity, such as a hairpin.
Our technique spans a wider range of human hairstyles than
previously proposed methods, and the styles generated by this
technique are fairly realistic.
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
