learnMatlab6

快速NCC(normalized cross correlation)算法实现。比matlab自带的normxcorr2 快10-20倍。在windows Unix环境下c语言，和matlab编译通过。(use Matlab on Windows or Linux or Mac OS X and want fast (exact, general-case) normalized correlation (NCC) code right now, then download it and enjoy the large performance gain over Matlab s normxcorr2 (demo included). For the brave (and/or masochistic) there is B), however, it is unlikely you will do better than the above. Thanks to Liang Jin for making the OS X binary (his performance comparison graph on a PowerPC). )

ce tp consiste a tatoué une image avec le principe de wong

Plots the polar spectrum of acceleration,velocity and displacement

数字图像处理，Matlab 6.5实现图像的腐蚀与膨胀。带有说明、例图与效果图。(Digital image processing, Matlab 6.5 implementation of the corrosion and expansion of the image. With that case diagram and effect diagram.)

说明：  本书详细介绍了偏微分方程的MATLAB解法，是解决偏微分方程的一种有效的数值解法。(The book details the MATLAB Solution partial differential equations, partial differential equations to solve an effective numerical method.)

小生境粒子群算法编程，及20篇相关论文，全都是关于小生境粒子群的研究与应用(Niche Particle swarm algorithm programming, and 20 article about NPSO s research and application)

ctp交易程序 ctp交易程序 ctp交易程序 ctp交易程序(ctp trading system)

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)

说明：  用于生成正交频分复用OFDM信号的Matlab仿真程序代码全面(Matlab simulation code for OFDM signals)

Digital Signal Processing Charles Darwin University Lab7