matllab_tips

MATLAB 下的snake算法实现 原始实现方法 检测眼睛、嘴巴(matlab snake algro )

通信原理实验代码，AM信号，单边带信号，双边带信号的产生以及傅里叶变换(Communication Theory test code, AM signal, single-sideband signal, as well as double-sideband signal generation Fourier transform)

有关matlab较为基础的入门书籍。很适合初学者学习借鉴和使用。(For more matlab basic introductory books. It is suitable for beginners to learn and use.)

：提出一种基于独立成分分析（ICA）和位隙动态分组技术的超高频（UHF）射频识别（RFID）多标签防碰撞算法。利用ICA 算法实现多目标的同时识别，利用动态位隙标签分组保证阅读器同时读取的标签数少于或等 于其天线数，从而使ICA 工作于非欠定状态(A novel ultra high frequency (UHF) radio frequency identification（RFID）anti-collision algorithm based on independent components analysis (ICA) and dynamic bit-slot grouping technology was proposed. The ICA algorithm was used for identifying several tags simultaneously. The dynamic bit-slot tags grouping technology was used for ensuring the number of tags read by a reader at the same time was less than or equal to its antennas’ number, and enabling the ICA wouldn’t work in an underdetermined state. Theoretical analysis and simulated experimental results show that the tags identification rate of the proposed algorithm is far more than that of traditional stochastic or deterministic TDMA RFID anti-collision algorithms.)

在matlab的环境下，对信号进行分析处理，设计滤波器 (In the matlab environment, an analysis of signal processing, filter design)

功能: 用基于拉格朗日函数Hesse阵的SQP方法求解约束优化问题:(Function: Hesse matrix based on Lagrangian SQP method for solving constrained optimization problems:)

预测人口时可以用到，用matlab编程计算出人口数据(Can be used to predict the population, population data calculated using matlab programming)

算法能实现数据的自动生成，有瑞士面包卷、双峰数据等(The algorithm can automatically generate the data, the Swiss rolls, doublet data,)

WINNER II模型matlab源代码 用于3D-MIMO仿真 包含三实例(WINNER II Model matlab source code for 3D-MIMO simulation Three examples included ;))

马氏距离是一种有效地计算两个样本集之间相似度的算法（数据之间协方差距离），与欧式距离相比，它考虑了各种特征之间的联系。本实验旨在通过给出的样本数据，设计一个最小马氏距离分类器并对测试点进行分类，然后将其与最小欧式距离分类器进行比较，实验得出当协方差矩阵为单位阵时，最小马氏距离分类器将与最小欧式距离分类器等价。本实验还根据实验样本设计贝叶斯分类器进行分类，并与最小马氏距离分类器进行比较，比较两种分类器的分类效果。(Markov distance is an effective method to compute the similarity between the two samples (data covariance distance), compared with the Euclidean distance, which takes into account the link between different characteristics. This experiment aimed at through the given sample data, design a minimum Mahalanobis distance classifier and classify the test points, and then compare it with the minimum Euclidean distance classifier. Experimental results showed that when the covariance matrix is a unit matrix, minimum Mahalanobis distance classifier with the minimum Euclidean distance classifier equivalent.In this experiment, according to the experimental design of the Bias classifier, and compared with the minimum distance classifier, the classification results of the two classifiers are compared.)