EDB (Evan s Debugger) is a QT4 based binary mode debugger with the goal of having usability on par with OllyDbg. It uses a plugin architecture, so adding new Features can be done with ease. The current release is for Linux, but future releases will target more platforms.
Release focus: Major Feature enhancements
Changes:
A new disassembly engine. A bug that could cause crashing was fixed. There is a new flags breakdown in the register view, an environment view plugin, and improved analysis (including a good speed increase). A bug in QT where disabled events could be triggered has been worked around.
Abstract:
By using gateway systems on large 32-bit platforms, networks of small, 8- and 16-bit
microcontrollers can be monitored and controlled over the Internet. With embedded Linux, these
gateways are easily moved from full-blown host PCs to embedded platforms like the PC104. In
this class you will learn about hardware platforms that support embedded Linux, Linux kernel
configuration, Feature selection, installation, booting and tuning.
The present document specifies the CAMEL Application Part (CAP) supporting the fourth phase of the network Feature Customized Applications for Mobile network Enhanced Logic. CAP is based on a sub-set of the ETSI Core INAP CS-2 as specified by ETSI EN 301 140 1 [26]. Descriptions and definitions provided by ETSI EN 301 140 1 [26] are directly referenced by this standard in the case no additions or clarifications are needed for the use in the CAP.
The problem of image registration subsumes a number of problems and techniques in multiframe
image analysis, including the computation of optic flow (general pixel-based motion), stereo
correspondence, structure from motion, and Feature tracking. We present a new registration
algorithm based on spline representations of the displacement field which can be specialized to
solve all of the above mentioned problems. In particular, we show how to compute local flow,
global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of
the above problems. Using a spline-based description of the flow removes the need for overlapping
correlation windows, and produces an explicit measure of the correlation between adjacent flow
estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D
projective scene geometry. We also provide results on a number of standard motion sequences.
μC/OS-II Goals
Probably the most important goal of μC/OS-II was to make it backward compatible with μC/OS (at least from an
application’s standpoint). A μC/OS port might need to be modified to work with μC/OS-II but at least, the application
code should require only minor changes (if any). Also, because μC/OS-II is based on the same core as μC/OS, it is just
as reliable. I added conditional compilation to allow you to further reduce the amount of RAM (i.e. data space) needed
by μC/OS-II. This is especially useful when you have resource limited products. I also added the Feature described in
the previous section and cleaned up the code.
Where the book is concerned, I wanted to clarify some of the concepts described in the first edition and provide
additional explanations about how μC/OS-II works. I had numerous requests about doing a chapter on how to port
μC/OS and thus, such a chapter has been included in this book for μC/OS-II.
Foundations of Python Network Programming leads readers through every aspect of client and server programming with Python. Python is a new and innovative scripting language. It is set to replace Perl as the programming language of choice for scripters and for serious application developers wanting a Feature rich yet simple language to deploy their products in.
This book Features extensive examples that not only demonstrate important concepts and practices, but also provide a cadre of fully-functional stand-alone programs. Readers can use the examples as the basis and building blocks for their own software
AdaBoost is an efficient tool in machine learning. It can combine a series of weak learners into a strong learner. Besides pattern classification, it also can be applied into Feature selection. This document explains the use of AdaBoost.
This Two-Category Classifier Using Discriminant Functions to
separeate two classes. The Classifier is designed on classes which
has two Feature vectors and other case it has one Feature vector.
ATViewer is a component for Delphi/C++Builder, which allows to view files of various types. There is no edit possibility, just quick view - so it s useful to implement "View file" Feature in your Delphi/C++Builder application.
The following view modes are implemented:
Text, Binary, Hex, Unicode: all files, of unlimited size. Plain file dump is shown. Used ATBinHex component: only visible part of file is loaded into memory. Modes are suitable for huge files and files of unknown type.
RTF/UTF-8: RTF and UTF-8 encoded texts. File is shown using RichEdit control.
Image: all general image formats: BMP JPG ICO GIF PNG... File is shown using ATImageBox component.
Multimedia: all file types supported by MS Windows Media Player: AVI MPEG WMV MP3... File is shown using WMP ActiveX control.
Internet: all file types supported by MS Internet Explorer: HTML XML DOC XLS... File is shown using MSIE ActiveX control.
Plugins: all files supported by Total Commander Lister plugins.
