Exercise4

This contains matlab code for rough fuzzy svm.It is done purely in matlab and only for binary case

工程师和科学家们面临着用更少的时间建立复杂系统模型的需求，他们使用分布式和并 行计算来解决高性能计算的问题。这些分布式的环境由多处理器和多核计算机来实现。 Mathworks公司开发的分布式计算工具箱可以在多处理器计算环境中使用 MATLAB 和imulink 解决计算、数据密集型问题。可以使用该工具箱解决通过装配多个处理器包含几个单独工作单位或单个大型计算的问题。这些处理器可以驻留在一个多处理器计算机，或者，当工具箱配合 MATLAB 分布式计算引擎时，驻留在计算机集群上。(Engineers and scientists are facing with less time to build models of complex systems needs, they use distributed and parallel computing to solve the problem of high-performance computing. The distributed environment consists of multi-processor and multi-core computer to achieve. Mathworks Distributed Computing Toolbox, developed in multi-processor computing environment MATLAB and imulink solve computing, data intensive problems. You can use the toolkit to solve multiple processors by assembling several separate work units or containing a single large-scale computing problems. These processors can reside on a multi-processor computer, or, when the toolbox with MATLAB Distributed Computing Engine, it resides on a computer cluster.)

对信号加窗，进行短时傅里叶变换，输出一个结构组，包含时间和频谱幅度(Signal windowed short time Fourier transform, and output a structure that contains the time and spectral amplitude)

给出了基本的有监督的基于矩阵分解的链路预测的代码。(Supervised link prediction based on matrix decomposition)

NFC读写测试demo，自动读取每个扇区每个块的值(NFC reader test demo, automatically reads each sector value for each block)

图像处理，用于在MATLAB中播放AVI视频文件。附送标准AVI文件一枚。(Image processing, used to play AVI video files in MATLAB. With a standard AVI file.)

电力系统3机系统的暂态稳定仿真程序，子程序为故障前故障中和故障后三个状态(System for power system transient stability simulation program, subroutine, three failures before the failure and fault status)

test tapi applications

Matlab Image processing ztransformLPF

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)