EKF

包括多种经典频谱估计程序，Matlab平台实现(Classical spectrum estimation procedures, Matlab platform)

Chi Twichiya Khassék télécxhargéha darooooooori

一个小而有效的程序来执行的K -近邻搜索算法，此算法利用JIT 理论加速循环，比向量化有效解决了大量数据的精度问题。甚至比kd-tree效果要佳(A small and effective procedures to implement the K- nearest neighbor search algorithm, this algorithm uses JIT acceleration cycle theory, than to solve a lot of data to quantify the accuracy of the problem. Even more than the kd-tree effect has to be good)

prim 最小生成树 代码 matlab平台使用(prim tree code matlab)

MATLAB code to calculate eigen engenry and eigen function of a square potential well.

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!

灰色系统模型的matlab实现，统计分析时不可或缺的一种模型。通过此m文件直接预测分析所需数据(Matlab implementation of the gray system model, an indispensable model in the statistical analysis. This m file directly predictive analysis required data)

人耳滤波器 Gammatone带通滤波器(Gammatone filter)

This toolbox contains Matlab files that implement the Laplacian pyramid (LP) decomposition and the new reconstruction method(This toolbox contains Matlab files that implement the Laplacian pyramid (LP) decomposition and the new reconstruction method)

离散小波变换，包括各层分解，消除了边界效应等问题(DWT，including every layer)