MAXDEV

数字信号处理 邮椭圆形变成双线性变换型 滤波器(Post Oval digital signal processing into bilinear transform Filter)

用ica进行信号处理，能有效的分离出源信号(Ica with the signal processing, can efficiently separate the source signals)

code for sattelite calculaton

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.

说明：  非线性有限元程序，二维， 非常好用的啊！！！！！！！！！！！！！！！！！！！！！！！！！！！！(nonliear fem)

图像增强—频域滤波 2．利用MATLAB提供的低通滤波器实现图像信号的滤波运算，并与空间滤波进行比较。 3．利用MATLAB提供的高通滤波器对图像进行处理。(Image enhancement- frequency domain filtering 2. Using MATLAB low-pass filter to provide an image signal filtering operation is compared with the spatial filtering. 3. Using MATLAB provides a high-pass filter for image processing.)

说明：  模拟化工生产精馏开工过程,运行软件为MATLAB(Simulation of chemical production to start the process of distillation, operating software for MATLAB)

MATLAB学习与应用，便于同学掌握其相关功能，实现在学习中熟练应用(Learning and application of MATLAB to facilitate the students to master their related functions, to achieve proficiency in the application of learning)

isodata聚类算法用于聚类类IRIS数据，分类效果较好，运行稳定(isodata clustering algorithm for clustering classes IRIS data classification better, stable operation)

Modelo de dos rayos de Andrea Goldsmith (figura 2.5, según expresión 2.12) f = 0.9 frecuencia en GHz landa = 0.3/f en m R = -1 coeficiente de reflexión en tierra ht = 10 altura del transmisor en m hr = 2 altura del receptor en m Gt = 1 Gr = 1 Pt = 1 d=10:.1:100000 phase_diff = 4*pi*ht*hr./(landa*d) aproximación Pr = Pt*((landa/(4*pi))^2)*((abs((sqrt(Gt)./d) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff)))./d)).^2) Pr = Pt*((landa/(4*pi))^2)*(1./d).*((abs((sqrt(Gt)) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff))))).^2) figure(1), clf, plot((d),10*log10(Pr/max(abs(Pr)))) grid xlabel( log_1_0(d) ) ylabel( Potencia recibida (dBm) )