MATLAB

leach算法MATLAB仿真，程序代码详细，完全能运行(leach algorithm MATLAB simulation, detailed program code, is fully operational)

Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!

说明：  basic concepts of power system in simulink 6-arabic

3GPP标准Turbo码编码译码仿真Matlab6.5及VC6.0源代码，并包含Turbo码编码译码原理的介绍文档。(3GPP standard Turbo coding and decoding simulation VC6.0 Matalab 6.5 source code , and includes Turbo Encoder Decoder description of the principle of documentation.)

Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy.

Hidden-Unit Conditional Random Fields 工具箱，可以用于训练linearCRF和和L.J.P. van der Maaten, M. Welling 提出的huCRF(We provide Matlab code that implements the training and evaluation of hidden-unit CRFs, as well as code to reproduce the results of our experiments. The code implements four different training algorithms: (1) a batch learner that uses L-BFGS, (2) a stochastic gradient descent learner, (3) an online perceptron training algorithm, and (4) an online large-margin perceptron algorithm. The code can also be used to perform (conditional) herding in hidden-unit CRFs. )

there are many code in matlab language about the classification used SVM algorithm

说明：  这个程序是我自己编写的小车倒立摆经典PID控制和模糊控制matlab程序，相信对初学模糊控制的朋友有不小的帮助，包含传统PID控制和模糊控制的对比，是一份内容详细的完整报告(This program is written in my own car classic inverted pendulum PID control and fuzzy control matlab program, I believe beginner friends of fuzzy control no small help, with the traditional PID control and fuzzy control of contrast, is a complete and detailed report of)

基于一阶AR模型u(n)=0.99u(n-1)+v(n),白噪声方差0.93627.步长0.05.分别使用M=2和M=3抽头的滤波器，用LMS算法实现u(n)的线性预测估计。并附仿真图已被参考。(Based on a first-order AR model u (n) = 0.99u (n-1) the+v (n), the white noise variance 0.93627 step 0.05. Respectively with M = 2 and M = 3-tap filter, with the LMS algorithm u (n) of the linear prediction estimates. Along with simulation diagram reference.)

说明：  在过去的几年中, 背包问题吸引了众多理论研究人员和应用工作者的注意力, 因而得到了广泛而深入的研究。理论方面, 研究兴趣主要来自于该问题简单的结构, 而这种特点既可以深入探索许多组合特性, 又可以通过解决一系列背包子问题来最终求解更为复杂的优化问题。实践方面, 这些问题可以表述许多工业场合的应用, 最典型的应用包括货 物装载、存储分配和资本预算等等。在寻找背包问题可行解的过程中, 出现了众多的算法, 本文是利用MATLAB 程序求背包问题可行解的一种算法。(In the past few years, knapsack problem has attracted many researchers and application of theoretical attention of workers, which has been extensive and in-depth study. Theory, research interest mainly from the simple structure of the problem, and this characteristic combination of both in-depth exploration of many features, but also can solve a series of knapsack sub-problems to solve more complex final optimization problem. Practice, these problems can be expressed in many industrial applications where the most typical applications include cargo loading, storage, distribution and capital budgets, and so on. Knapsack problem in finding feasible solutions in the process, there have been many algorithms, this is the knapsack problem using MATLAB program seeking a feasible solution algorithm.)