matlab-gui

用matlab 实现强噪声中弱信号的提取，可产生图像 一次生成(Using matlab to achieve strong noise in the extraction of weak signal can produce a generation of images)

生成数字信号处理课程常用信号：冲激、阶跃、斜坡、正弦、复指数和指数信号，幅度长度时延频率相位可控。(Signal generation, capable of generating pulse, step, ramp, sine, complex sine and exponent signals.)

说明：  用Matlab编写的自动多阈值分割程序，分割图像之前采用自动平滑图像(prepared using Matlab automatic threshold segmentation procedures, image segmentation before the introduction of automatic image smoothing)

说明：  Matlab学习宝典 解方程 Matlab学习宝典(Matlab solutions of equations Matlab Baodian learning learning Baodian)

使用MATLAB进行编程，实现一个带通滤波器(coding by MATLAB，realize a band- pass filter)

说明：  使用yalmip调用CPLEX求解器求解优化问题(Using yalmip to call CPLEX solver to solve optimization problems)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

四维状态、二维观测的目标跟踪扩展卡尔曼滤波程序，附有详细的说明,便于新手学习。(The four-state, two-dimensional observation of the target tracking extended Kalman filtering procedures, with detailed instructions, easy for novices to learn.)

小波分析因其在处理非平稳信号中的独特优势而成为信号处理中的一个重要研究方向。如今随着小波理论体系的不断完善，小波以其时频局部化特性与多尺度特性在图像边缘检测领域中倍受青睐。(Wavelet analysis of nonstationary signal processing because of its unique advantage in signal processing and become an important research direction in. Now with the wavelet theory system ceaseless and perfect, the time-frequency localization characteristic of wavelet based and multi scale characteristics in the image edge detection is very popular.)

Subdivision interpolation using cubic B-spline wavelet.