BandPassF

自己编写的卷积和打孔代码，已通过Matlab函数验证。(Convolutional encoding and puncturing 4/6, verified.)

用于libsvm数据转换，使得特征矢量按规定的方式转换(Libsvm for data conversion, making feature vector in accordance with the provisions of the mode switch)

matlab编写的禁忌搜索算法，解决了了TSP问题，对初学入门者有重要的参考价值 (Matlab prepared by the tabu search algorithm to solve TSP problems have important reference value for beginners beginners)

有限元中比例nearQuad单元的matlab程序(FEA matlab )

用于激光谐振腔的计算，采用早期的foxli算法。

本程序为emd的基本程序，可以通过调用此程序，对信号进行emd分解(The procedure for the emd of the basic procedures, you can call this procedure, the signal emd decomposition)

aac compression its not complete, but i make it with step from block diagram

Detect arrows(or other features) in a diagram

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算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(algorithm MUSIC is a method based on matrix feature space decomposition. From a geometric point of view, the signal processing of the observation space can be decomposed into signal subspace and noise subspace, it is clear that these two spaces are orthogonal. Signal subspace by the array received data covariance matrix and signal corresponding to the feature vector and noise subspace is by the covariance matrix of all minimum feature values (noise variance) corresponding to the feature vectors are composed. MUSIC algorithm is to use the orthogonal characteristics of these two complementary space to estimate the azimuth of spatial signals. All vectors of the noise subspace are used to construct spectral, and the peak position in all spatial azimuth spectra correspond to the direction of arrival of the signal. MUSIC algorithm greatly improves the direction finding resolution, and at the same time it is suitable for the antenna array of any shape, but the prototype MUSIC algorithm requir)

病态线性方程组的计算题，涉及Gauss消元法、雅可比迭代法、高斯-赛德尔迭代法、最速下降法和共轭梯度法。每一个方法，都编写一个m文件，封装成函数的形式。然后通过总的HilbLineEquSet.m文件来调用执行，画出误差曲线图，得到运行结果。总的Matlab程序流程，如下所示： 病态方程组的计算包括：HilbLineEquSet.m、gauss.m、jacobi.m、gauss_seidel.m、fastest_descend.m和conjugated_grad.m六个文件。 程序执行结果包括：求解结果、迭代次数、迭代误差数据、误差曲线图等。 (Morbid Linear Equations calculation problems involving Gauss elimination method, Jacobi' s iterative method, Gauss- Seidel iterative method, steepest descent method and conjugate gradient method. Each method, all the preparation of an m file, packaged in the form of a function. Then the total HilbLineEquSet.m file to invoke the implementation of draw error curve, to be running results. General Matlab program flow, as follows: the calculation of morbid equations include: HilbLineEquSet.m, gauss.m, jacobi.m, gauss_seidel.m, fastest_descend.m and conjugated_grad.m six documents. Procedures for implementation of the findings include: solving a result, the number of iterations, iterative error data, error curve and so on.)