FaceSpace

STT using matlab, back groud reconstruct(STT using matlab,back groud reconstruct)

该程序是用于求解信赖域子问题的Powell dogleg方法(This routine solves the trust region subproblem by Powell s dogleg method.)

This is a simple program to calculate the output of artificial neural network (ANN) using microcontroller ATMega 32. Assume that the neural architecture is : 2 hidden layers with 4 and 2 neurons respectively and 1 layer output with 1 neuron.This program explains the step how to compute the ANN in off-line mode.Means the weights and biases are already exist (microcontroller is not doing the learning steps). These parameters are produced using MATLAB. Therefore, we have already made the appropriate architecture using MATLAB. The tansig function is use in both of hidden layer. Finally, the neural output will be displayed on LCD port C.

最近三年的关于小波包分析的研究文献资料，知网权威，研究小波包的可以下载借鉴(recent three years of research on wavelet packet analysis literature, authority of hownet, study of wavelet packet can download for reference )

最优阵列处理,阵列信号处理方面的程序,其中有许多子程序可用.(Optimal array processing, array signal processing procedures, many of which subroutines are available.)

基于递归最小二乘算法在线辨识锂离子电池一阶模型参数。(The first order model parameters of lithium ion battery are identified online based on recursive least square algorithm.)

QR Decomposition for non quadratic system

基于matlab的多孔算法，matlab，matcom(based on the porous Matlab algorithms, Matlab, matcom)

MATLAB simulation of OFDM transmitter and reciever

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!