SINS-matlab-solver-

Design of a Gist Descriptor. Computing gist requires 1) prefilter image, 2) filter image and collect output energies

本程序可仿真IEEE802.16e LDPC编解码器，程序设计原理见本人如下专著。 [1] 肖扬著, Turbo与LDPC编解码及其应用,人民邮电出版社，2010. [2] 肖扬著, MIMO多天线无线通信系统，人民邮电出版社，2009。 本程序只用于科研，不得用于其它场合。(The program can simulate LDPC codec with IEEE 802.16e LDPC of the size 2256*4512, and 1/2 code rate. Copyright (C2010-2015) Yang XIAO, Beijing Jiaotong University, Nov.3, 2010, E-Mail: yxiao@bjtu.edu.cn. Current Version Published: 2010-11-3 In my following books, I derived the algorithm of constructing the parity check matrices of IEEE 802.16e LDPC. [1] Y. Xiao, Turbo and LDPC Codecs and their Applications, Press of Posts and Telecommunications, Beijing, 2010. [2] Yang Xiao, MIMO Multiple Antenna Wireless Communication Systems, Press of Posts and Telecommunications, Beijing, 2009. This program also provides the LDPC code with the size 4512*2256, and 1/2 code.)

无线传感网络一种分布式定位算法的简介，介绍详细，并带有MATLAB仿真。(A distributed wireless sensor network localization algorithms brief description in detail, and simulation with MATLAB.)

this is matlab program for showing the result of quasi ostbc

平面阵列的波束形成的相关图像及代码，包括等高线，极坐标三维图等(Planar array beamforming related image and code, including contour, polar coordinate dimensional map)

This the mathematical computational method of step-vary Gill method.

基于有限混合高斯模型的数据分类 1、使用基于有限高斯混合模型的EM算法对数据样本进行归类 2、使用C++或者Matlab语言编程环境实现该算法，并用给定的数据包对算法的正确性进行检验 (Gaussian mixture model based on limited data classification 1, using the finite Gaussian mixture model based on EM algorithm to classify the data samples 2, using the C++ or Matlab programming environment to realize the algorithm, and with a given data packet to the algorithm correctness testing )

利用matlab开发平台和相关的SVM优化工具箱,及OAO,OAA,BSVM2算法模型,设计改进并实现非线性的模式分类实验模型系统.(Matlab use development platform and related optimization toolbox of SVM, and OAO, OAA, BSVM2 algorithm model, designed to improve and to achieve non-linear model of the pattern classification system.)

IMM-交互多模卡尔曼滤波机动与非机动模型算法实现(IMM-interactive multi-mode Kalman filtering motorized and non-motorized model)

说明：  遗传算法优化的极限学习机模型 采用水仙花基本特征数据集 效果比单纯的ELM模型要好(The effect of using daffodils basic feature data set in the extreme learning machine model optimized by genetic algorithm is better than that of ELM model only.)