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
Input
The first line of the input contains a single integer T (1 <= T <= 20), the number of test cases. Then T cases follow. The first line of each case contains N, and the second line contains N integers giving the time for each people to cross the river. Each case is preceded by a blank line. There won t be more than 1000 people and nobody takes more than 100 seconds to cross.
Output
For each test case, print a line containing the total number of seconds required for all the N people to cross the river.
Sample Input
1
4
1 2 5 10
Sample Output
17
Zinc is an object oriented, platform independent, framework for designing
graphical user interfaces (GUIs) for cross platform applications. Zinc is scalable
and customizable. It provides a full-featured toolkit for the creation of GUIs and
event-driven applications.
FIST可堆疊文件系統，linux文件系統修改相關的開源軟件，使得對linux文件系統功能的改進很簡單了
The FiST (File System Translator) system combines two methods to solve the above problems in a novel way: a set of stackable file system templates for each operating system, and a high-level language that can describe stackable file systems in a cross-platform portable fashion. Using FiST, stackable file systems need only be described once. FiST s code generation tool, fistgen, compiles a single file system description into loadable kernel modules for several operating systems (currently Solaris, Linux, and FreeBSD). The project demonstrates that with FiST, code size and development time are reduced significantly, while imposing a small performance overhead of only 1-2％. These benefits are achieved, as well as portability, without changing existing operating systems or file system.
These Simulink blocks contain transfer functions that model the pressure and flow transients for axisymmetric 2D viscous flow of a compressible fluid in a straight rigid circular cross section pipelines. Three models are available:
(1) pressures at the ends
(2) flow rates at the ends
(3) pressure at one end and flow rate at the other
Filtering is incorporated to reduce numerical oscillation (Gibbs phenomenon). See J. Dyn. Systems, Meas. & Control vol 122 (2000) pp. 153-162.
【下載說明】
這里提供給大家的是《Embedded Linux: Hardware, Software, and Interfacing》（嵌入式 Linux---硬件、軟件與接口）一書的英文原版CHM格式下載。
【作者簡介】
Craig Hollabaugh has been fascinated by electronics since he bought an AM radio in elementary school. He was first exposed to Unix during a cross-country talk session in 1985. Later, he administered networked Sun and DEC workstations while pursuing a doctoral degree in electrical engineering at Georgia Institute of Technology.
【內容提要】
本書通過一個冬季旅游勝地自動化管理項目實例，從軟件、硬件和接口的觀點介紹嵌入式Linux。引入項目需求后，作者講述了開發環境的建立，接著用一系列軟硬件接口實例展示了如何使用異步串行通信、PC并口、USB、內存I/O、同步串行通信以及中斷，等等。最后介紹了將前面所有的工作有機地組織在一起的系統集成過程。本書以實際應用為導向，書中整個項目的實施過程和軟硬件接口實例都具實踐指導意義。
runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = initial covariance of observation sequence noise
returns model parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
