dtw

本代码实现了利用matlab仿真产生PAM-DS-UWB发射波形的过程。(The code generated by using matlab simulation launch PAM-DS-UWB waveform process.)

本人做的GPS整个基带的捕获模型.用的是SIMULINK,捕获方式采用码并行捕获,其中信号源调整了P码(entire GPS base-band model of the capture. Simulation is used. Capture mode using parallel code capture, which adjusted the signal source code P)

程序展示了bayes classifier对于连续数据的应用，假设数据均服从高斯分布。程序包含了binary classification和multi-classification的例子。(Program shows bayes classifier for continuous data applications, assume that the data are Gaussian. Program includes a binary classification and multi-classification examples.)

基于BP_Adaboost的强分类器设计-公司财务预警建模(Based BP_Adaboost strong classifier design- corporate financial early warning modeling)

说明：  压缩包内包涵了我所用过的图像融合程序，如小波，PCNN，贝叶斯等算法，仅供参考。(Compression package contains the image fusion procedures I used, such as wavelet, PCNN, Bayesian algorithm, for reference only.)

matlab数字图像处理中的自适应中值滤波函数（.m）(adaptive median filter function file in matlab)

Phase-based Opic Flow Algorithm, described in Gautama, T. and Van Hulle, M.M. [2002]. A Phase-based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering. IEEE Trans. Neural Networks, 13[5], 1127--1136.

说明：  作为初学者的典范,但是程序有很些许错误，还希望高手指点，特别是运行时，每次出的结果不一样，更希望高手指点！(this project is just so so ,it can be used to study by the first learner!certainly ,there are some misstakes, but i can not find it ,because my level is just so so ,i hope you can correcting me !thank you very much !if you correct my project ,pleased send email to hljdx.wuyou@163.com ,thanks! i found my project ,there are so many results when i run ,i do not know why?)

Particle swarm optimization has been used to solve many optimization problems since it was proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and several papers on this topic [5][7][13][15], one of which did a study on its performance using four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO, each particle moves in the search space with a velocity according to its own previous best solution and its group’s previous best solution. The dimension of the search space can be any positive integer.

blind equalization for adaptive filtering