daopufenxi

matlab code for image processing and segmentation

室内指纹定位算法，内有NN,KNN,WKNN多种算法进行对比，可运行，有注释。(Indoor fingerprint localization algorithm with NN, KNN, WKNN comparing various algorithms, can run, with comments.)

仿真是现代雷达系统设计成功的基础，从这一点来说，没有任何软件比MATLAB更好。本代码提供雷达系统仿真常用MATLAB程序，内容包括：雷达基础导论、雷达检测、雷达波形、雷达模糊函数、脉冲压缩、面杂波与体杂波、动目标显示和杂波抑制、相控阵、目标跟踪、电子对抗、雷达截面积、高分辨率战术合成孔径雷达、信号处理等，提供MATAB文件和雷达系统设计与分析所需要的实际仿真经验(Simulation is a modern radar system design basis for success, from that point, there is no software better than MATLAB. The code provides radar system used MATLAB simulation program, including: Introduction to basic radar, radar detection, radar waveforms, radar ambiguity function, pulse compression, face and body clutter clutter, moving target indication and clutter suppression, phased array, target tracking, electronic warfare, radar cross section, high-resolution synthetic aperture radar tactics, signal processing, simulation provides MATAB documents and practical experience in radar system design and analysis needed)

加热炉 非线性模型预测控制 多输入多输出 simulink建模(reactor nonlinear model predictive control mimo Simulink modelling)

说明：  这是一个基于空时分组编码的MIMO_OFDM通信系统的仿真设计。此系统包括QPSK调制解调，IFFT调制，空时编解码，基于训练符号的信道估计等通信模块。 (This is a space-time block coding based on the MIMO_OFDM Communication System Simulation and Design. This system consists of QPSK modulation and demodulation, IFFT modulation, space time coding and decoding, based on training symbols such as communication channel estimation module.)

说明：  利用matlab编程进行参数检验，点估计，区间估计，以及非参数检验(Using matlab programming parameter testing, point estimation, interval estimation, as well as non-parametric test)

三相静止坐标系与两相静止坐标系之间的变换关系(Clarke变换) (Three-phase static coordinate system and two phase transformation relationship between static coordinate system (Clarke transformation）)

计算指数平滑模型预测及预测值与真实值的统计量计算(Exponential smoothing model and prediction value statistic calculation)

雷达波形,matlab代码，波形建模，GUI界面(Radar waveform,matlab code, waveform modeling, GUI interface)

说明：  选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)