Micro In-System Programmer Brief Installation Notes
Enter the src directory.
If uisp does not compile successfully, add switch -DNO_DIRECT_IO in the
Makefile to remove support for direct I/O port access (that may be
necessary on non-PC architectures). Parallel port access should still
work if you have the Linux ppdev driver (patch for 2.2.17 is in the
kernel directory, ppdev is standard in 2.4 kernels). Please lobby
Alan Cox to include this tiny little driver in 2.2.x too :).
To make it type:
make
and to install it:
make install
If you have any further doubts, please consult UISP s homepage:
http://www.nongnu.org/uisp/
this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.
Easy to use SMS/MMS Messaging Gateway to develop Content Delivery Platforms by GSM Operators,Content Providers and even non-telecom guys. Uses file system for data flow.Written for .NET 2.0 in C#
Math.NET開(kāi)源數(shù)學(xué)庫(kù)
C#實(shí)現(xiàn)
具體功能:
- A linear algebra package, see MathNet.Numerics.LinearAlgebra.
- A sparse linear algebra package, see MathNet.Numerics.LinearAlgebra.Sparse.
- Non-uniform random generators, see MathNet.Numerics.Generators.
- Distribution fonctions, see MathNet.Numerics.Distributions.
- Statistical accumulator, see MathNet.Numerics.Statistics.
- Fourier transformations, see MathNet.Numerics.Transformations.
- Miscellaneous utilies (polynomials, rationals, collections).
OpenCV means Intel® Open Source Computer Vision Library. It is a collection of C functions and a few C++ classes that implement some popular Image Processing and Computer Vision algorithms.
OpenCV has cross-platform middle-to-high level API that consists of a few hundreds (>300) C functions. It does not rely on external libraries, though it can use some when it is possible.
OpenCV is free for both non-commercial and commercial use (see the license for details).
OpenCV provides transparent interface to Intel® Integrated Performance Primitives (IPP). That is, it loads automatically IPP libraries optimized for specific processor at runtime, if they are available. More information about IPP can be retrieved at http://www.intel.com/software/products/ipp/index.htm
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本文是opencv的入門教程
Listed below are the typographical conventions used in this guide.
– Example C++ code and commands to be typed by the user are in non-bold characters in typewriter
font.
– Items where the user has to supply a name or number are given in lower-case italic characters in
typewriter font.
– Sections marked with a ‡ describe features that are also available in ANSI C.
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tServer task executes functions at low priority (254). serverSend
funtion is used to send a request to the tServer to execute a
function at the tServer s priority.
First tServer task executes the i() system call to print the summary
of tShell task. Next an address exception is caused in tServer
task by trying to execute a funtion at non-aligned word boundry.
As a result SIGBUS signal is raised automatically. Signals
(setjmp() and longjmp()) are used to allow the tServer task to
recover gracefully from an address error exception. For more
information about signals, please refer to sigLib manual pages.
Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheshadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and statistical parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-dependence,?exibilitytoshadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
