CH4

说明：  8，16，64进制的qam 星座图转换，MATLAB程序(8,16,64 qam constellation hex conversion, MATLAB program)

Image Enhancement for enhancement the image by using the digitneg for enhancement the image by using the digitneg

不同坐标系下相同测量点之间的四元数转换矩阵求解，可以此来求解坐标系A到坐标系B之间的转换矩阵(Different coordinates between the same measurement points quaternion transformation matrix solution can be used to solve this coordinate system A to coordinate system transformation matrix between B)

使用matlab语言编程解决整数规划问题(Using matlab language programming to solve the integer programming problem )

Introduction to ZigBee standard and research papers for the same is attached.

基于arma模型分析方法的具体实现代码， 可以用，放心下载(Arma model-based analysis code, you can rest assured download)

对二进制传统搜索算法及二叉树和四叉树的混合算法进行了matlab程序仿真(Binary search algorithm and mixed traditional binary and quad-tree algorithm were matlab simulation program)

is a small example of how to use a slider and observe and enter the current value in the slider

自适应控制方面的经典书籍，仅供学习研究参考用，不得违规(Adaptive control of classic books)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.