Frequency-detection

使用matlab编程实现AR模型中的参数确定(AR model)

模拟掺饵光纤中的光功率变化代码 采用GILES模型(Analog erbium-doped fiber optical power change code GILES model)

利用模拟Butterworth滤波器设计数字低通滤波器！(Analog filter design using Butterworth digital low-pass filter!)

Matlab code for generation of signals

《空时无线通信导论》，第四章的所有图片的matlab的重现程序，和仿真结果。仿真程序包括MIMO信道容量、发射端不知晓信道信息时信道容量等程序。(" Introduction to space-time wireless communications," Chapter IV reproduce all the pictures of matlab procedures, and simulation results. The simulation program, including MIMO channel capacity, the transmitter does not know when the channel information channel capacity and other procedures.)

五种常用滤波函数，巴特沃兹低通滤波、中值滤波等(5 different functions of filter)

利用高斯-勒让德多项式计算 sin(x+y)在矩形区域的积分(use Gauss- Legendre polynomials calculated sin (x y) in the rectangular region of Integral)

matlab gui设计学习手册第三章源码(matlab gui)

many regression functions for different regression techniques

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.