这是一份关于Linux下usb驱动程序原理以及代码分析的的好文档

利用vb来实现显示滚动的字，是你的软件界面更美观-use to achieve the characters show rolling, is your software interface more beautiful

unix系统管理基础教程.是认证培训的基本教程,权威性较高.-based tutorial. Certification training is a basic guide, a higher authority.

使用VB和MAPX开发的一个比较通用的实例程序

rational rose 的一个例子，大家看看对UML有没有学习的好处！-rational rose an example, let us look at the UML have not learned the benefits!

... ...游戏 src......源代码 image....图片 -Src ...... ... ... the game source code image .... picture

东海李莲中学网页源代码下载学习指导参考文件-The East China Sea Lian-school study guide website to download the source code reference document

6502汇编器 要将6502汇编语言源文件汇编成二进制文件-Assembler 6502 to 6502 assembly language source files compiled into binary files

Embedded vc++下开发的可编辑地图程序-developed under the map editing procedures

这个是我学《windows 游戏编程技巧大师》，那里边的第一个例子。 不过，用了其中的一些代码,而且发现，书附带光盘里的代码残缺不全，不知道是不是作者故意让读者自己来填？ 我自己也写了很多代码。特别是代码组织上，自己觉得还可以.-this is me "windows game programming skills guru" and the edge where the first example. However, the use of some of these codes, but also finds that the incidental CD Lane code broken, I do not know if the author intentionally allow their readers to fill in? I also write a lot of code. In particular code organizations, and that they felt that I could.

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,