matleb

电压电流负反馈的仿真程序，用matlab语言实现，经过测试可以运行(Voltage and current negative feedback of the simulation program, using matlab language, tested can run)

matlab code pdf file fractal image compression techniques

在径向基函数拟合、径向基函数神经网络和小波神经网络中的几种常用的基函数。包括高斯基函数、逆二次基函数、小波基函数等。(Several basis functions in radial basis function fitting, RBF neural network and wavelet neural networks. Including Gauss function, inverse quadratic basis functions, wavelet function and so on.)

Same functionality as the MATLAB rref function

对UWB信道进行估计以及对UWB接收器的性能仿真。(simulation for UWB channel)

对多目标优化算法NSGA-II算法的改进，该算法进化代数少，但是获得的最终效果特别好！(pair of multi-objective optimization algorithm NSGA-II algorithm, the algorithm evolutionary less algebra, However, the ultimate effect was particularly good!)

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!

16位加法器，需要的拿去，经仿真试验成功的(16-bit adder is needed instead, after a successful simulation test)

计算八木天线的电流分布，方向图，输入阻抗(Calculated Yagi antenna current distribution, direction map, the input impedance)

Advanced correlation filter synthesis algorithms to achieve rotation invariance are described. We use a specified form for the filter as the rotation invariance constraint and derive a general closed-form solution for a multiclass rotation-invariant filter that can recognize a number of different objects. By requiring the filter to minimize the average correlation plane energy, we produce a multiclass rotation invariant (RI) RI-MACE filter, which controls correlation plane sidelobes and improves discrimination against false targets. To improve noise performance, we require the filter to minimize a weighted sum of correlation plane signal and noise energy. Initial test results of all filters are provided