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说明：  基于matlab进行图像的滤波，轮廓提取和角度计算的综合程序，经调试可以正常运行(Matlab for image-based filtering, contour and angle of the Composite process, the debugger can operate normally)

Calcula the PAPR and the CCDF of the ofdm signal

相控阵雷达的最优分区搜索算法 (Search algorithm for optimal partition of phased array radar)

这个文件编程实现了正弦调频的频谱图分布和乔伊-威廉斯分布，通过观察我们可以看到不同时频分布算法实现的不同特点(This file programming the frequency spectrum of a sinusoidal FM distribution and Joy- Williams distribution, we can see by observing the time-frequency distribution of the different features of algorithm implementation)

用于观察脑电信号全通道数据和多个单通道数据，并且输出相对颅内压参数。函数通过读取nt后缀脑电图文件，并生成GUI界面，可选择全通道观察所选文件脑电信号，也可以选择包含原始EEG脑电信号，40hzEEG，80hz肌电干扰和提取出的40hzEEG信号的多个单通道观察，并且生成颅内压参数。(Used to observe all channels of EEG data and multiple-channel data, and output parameters relative intracranial pressure. 40hzEEG signal function by reading nt suffix EEG file and generates a GUI interface, you can all channels of EEG observe the selected file, or you can choose to include original EEG EEG, 40hzEEG, 80hz EMG interference and extracted a plurality of single-channel observation, and generates an intracranial pressure parameters.)

说明：  此程序实现自适应波束图中的不同的阵列间距和期望信号方向对波束图的影响(Realize this procedure adaptive beamforming map different array spacing and the desired signal direction of the impact of beamforming map)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)

matlab with GUI code, for fourier transformation, gaussians low pass filter and gaussians high pass filter.

THIS IS A CURRENT DIFFERENTIAL PROTECTION SCHEME OF IEEE 39 BUS SYSTEM

基于matlab利用顺序滤波器方法进行语音端点检测的算法实现代码(Sequential filter method based on matlab voice endpoint detection algorithm code)