应用离散傅里叶变换DFT，通过MATLAB实现对连续信号频谱分析，以满足理论分析和工程实际的需要。...

matlab和vc++联合编程的例子，使用matcom编写matlab程序链接成库文件在vc++中使用。(matlab and vc++ joint programming examples, the use of matlab Matcom preparation procedures into a library file in the link vc++ use.)

关于锁相环路的相关仿真，描绘的是一阶锁相环，二阶及三阶的各个特性。(PLL simulation, depicted is a first-order phase-locked loop, the second-order and third-order characteristics.)

张家口一个风电场的离线预测，核心算法是神经网络(RBF on-line prediction of wind speed data in Zhangjiakou.)

非下采样轮廓波变换，NSCT变换，主要用于图像去噪，效果非常不错(Nonsubsampled contourlet transform, NSCT transform, mainly for image denoising, the effect is very good)

标准化切割算法既可以度量不同组之间的总不相似性，也可以度量组内的总相似性。令人惊奇的是，分裂点的最优解很容易通过求解一个广义特征值问题来计算。一种解决视觉中知觉分组问题的新方法。我们的方法不是关注局部特征及其在图像数据中的一致性，而是着眼于提取图像的全局印象。他们将图像分割视为一个图分割问题，提出了一种新的全局分割准则&归一化割。标准化割准则既可以度量不同组之间的总体差异性，也可以度量组内的总体相似性。结果表明，基于广义特征值问题的有效计算方法可以用来优化该准则。他们将这种方法应用于分割静态图像和运动序列

本程序的性能已经达到较高水平，本程序的性能已经超过其他算法，插值与拟合，解方程，数据分析，包括邓氏关联度、绝对关联度、斜率关联度、改进绝对关联度,详细画出了时域和频域的相关图，计算多重分形非趋势波动分析。

说明：  ct图像和磁共振图像的相互配准的matlab源代码(The Registration of CT and MRI images)

本书荣获2005年第十五届Jolt通用类图书震撼大奖。 继 Effective C++ 之后，Scott Meyers 于 1996 推出这本「续集」。本书与现今之 C++ 标准规格几乎相同。可能变化的几个弹性之处，Meyers 也都有所说明与提示。(the book won the 2005 session of the 15th General Books Jolt Awards shock. Following the Effective C, Scott Meyers in 1996 to launch the "sequel." The book's C with the current standard is almost the same. Several possible changes in flexibility, Meyers also has suggested and described.)

This file contains and display nancy s articulated humanoid body in anatomical static posture. Body segments can be accessed independently they are named with suffix: "_point", polygons are also independent with suffix: "_polygons". In addition, the variables "point" and "polygons" include the whole points and polygons respectively with the accumulative index in the case of polygons. After running the file, do a "whos" to see the actual names of body segments.

一个基于BP神经网络的matlab程序可以实现对几种字体0-9的数字识别这个文件训练网络的压缩包(Based on BP neural network matlab program can achieve several fonts on the figure 0-9 to identify the document training network packet compression)