seagrid

本例程对十字形32QAM信号进行盲均衡处理，码间干扰可以降低到-30db，输出星座图更清晰(The routines of cross blind equalization 32QAM signal processing, inter-symbol interference can be reduced to-30db, output constellation diagram more clearly)

说明：  读取obj文件…………，matlab程序(read obi of matlab)

张德丰《MATLAB 小波分析》一书中所有实验的源代码，希望对大家有所忙住啊！(Zhang Defeng " MATLAB wavelet analysis" book, the source code of all the experiments, we hope to busy live!)

在无线信道的理论基础上，分析了Suzuki信道模型的结构原理，介绍了利用正弦波叠加法构成高斯序列，从而建立Suzuki信道数学模型的方法，并通过Matlab软件对其进行了仿真。(In the theory of radio channel based on the analysis of Suzuki channel model structure principle, describes the use of sine wave superposition method constitutes a Gaussian sequence, thereby establishing a channel model Suzuki method, and through its simulation Matlab software.)

哈夫变换检测圆，，检测一个半径为80，灰度值为160的圆，它处在灰度值为96的背景中(Hough transform to detect circles, and testing a radius of 80, round gray value of 160, it is in the background gray value of 96)

自适应滤波算法的LMS算法的仿真验证程序是在FM信号上提取完成的(LMS adaptive filtering algorithm of the simulation algorithm the verification process is complete extraction of FM signal on the)

DEA模型的MATLAB算法,DEA-Solver.rar(DEA MATLAB)

说明：  自适应滤波和仿真实验，有毕设的同学可以参考下(Adaptive filtering and simulation experiments, there is complete set up for students to refer to the following)

感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)

modelisation machine asynchrone