LMD分解

这是一个虹膜识别检测的周围元器件的程序描述，很完整，也是很好验证的(This is a test of iris recognition around the components of the program description, it is complete and is good for validation)

说明：  WCDMA链路仿真，包括源代码与相关文档资料。(WCDMA Link Simulation, including source code and related documentation.)

NIT工具箱升级版，包括二重积分的任意区间（采取的是高斯-勒让德积分）(NIT toolbox )

Malla de PAT proyecto

这本书适用于对于C++和Matlab均有一定了解的基础上进行阅读。很好的将两种语言联系在了一起。(This book is applicable to the C++ and Matlab have a certain understanding on the basis of reading. Very good in both languages will be linked together.)

改程序为基于核函数的主成分分析算法的MATLAB实现程序,对于学习KPCA有很大帮助(This program is the MATLAB program which is based on the kernel principal component analysis )

The 802.11 standard specifies a carrier sense multiple access with collision avoidance(CSMA/CA) protocol. In this protocol, when a node receives a packet to be transmitted, it first listens to ensure no other node is transmitting. If the channel is clear, it then transmits the packet. Otherwise, it chooses a random "backoff factor" which determines the amount of time the node must wait until it is allowed to transmit its packet. During periods in which the channel is clear, the transmitting node decrements its backoff counter.

IEEE802.16系统规定的信道模型 适用于城市环境的SUI-3模型是WIMAX系统信道建模时的必备(Requirements for IEEE802.16 channel model applicable to the urban environment of the SUI-3 model is a WIMAX system channel modeling essential)

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcomings of the steepest descent method and avoids the need for the Newton method to store And the calculation of Hesse matrix and the inverse of the shortcomings, conjugate gradient method is not only to solve large-scale linear equations one of the most useful methods, but also solution for large-scale nonlinear optimization of one of the most effective algorithm. In a variety of optimization algorithms, conjugate gradient method is a very important one. The advantage is that the required storage capacity is small, with step convergence, high stability, and does not require any external parameters.)

it describes about the Bwt transform for image