FM

nv_encd.m　卷积编码程序 viterbi.m　　卷积译码程序 其它的是viterbi.m中用到的子函数 程序来自《现代通信系统－使用matlab》英文版 已经调通！并加上了注释。 希望对大家有帮助 GOOD　LUCK！(nv_encd.m convolutional coding procedures viterbi.m convolutional decoding process viterbi.m other is used in Function procedures from the )

一个四维状态、二维观测的目标跟踪扩展卡尔曼滤波程序，附有详细的说明。(a four-state, two-dimensional observation of the target tracking extended Kalman filtering process, with detailed explanations.)

施瓦茨变换的代码，专门用于矩形区域的变换，matlab代码(The Schwartz transform code, specifically for the transform of the rectangular area, matlab code)

悬置功能计算的，能量解耦的方法，优化及其位移的计算(The calculation of suspended function, energy decoupling method, optimization and calculation of displacement)

Plot historical stock data (m file)

关于弹簧质量块的一个误差空间控制Matlab仿真程序( SPRING MASS SYSTEM SIMULATION FOR ERROR SPACE CONTROL )

MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].

利用MMSE_MSE实现信道估计（matlab源代码）(channel estimation using MMSE_MSE)

这是有关,matlab的各种窗函数的设计事例.(It is about, matlab window function of various design examples.)

曲线模型的半自适应交互多模型跟踪方法 我存的一篇论文，拿来与大家共享(When using interactive multiple model (IMM) to track targets,to select smallermodel set to better cover the move- ment property and the maneuvering level of various targets and meanwhile to reduce computation complexity are both worthwhile and also necessary research tasks to project implementation.The main work is done through the IMM algorithm of curvilinear model,the construction of angle velocity estimation model and the extension of it to state equations,whichmake it possible for normal acceleration to be self-adaptively adjusted,hence it reduces the model setto associate onlywithMcurvilinearmodels selatingtotangential acceler- ation.Computation burden is reduced and algorithm’s applying range is extended.The simulation further proves the conclusion )