hai_myphamvn

主要计算声学里的 A计权中各频率点的修正值，自动计算得出修正值。(It mainly calculates the A weight of the acoustics, the correction value of each frequency point, and automatically calculates the correction value.)

这个源码是从其它网站上转载来的，希望对大家有所帮助－－锅炉热力计算程序(c语言版)！-from this source is reproduced on other websites to, We hope to help-- the boiler thermal calculation (c language version)!

基于安卓平台的车牌识别系统的实现，使用JAVA语言进行操作与实现，运行环境为JDK(Based on Andrews platform license plate recognition system implementation, the use of JAVA language operation and implementation, the operating environment for the JDK)

说明：  主要对锁相环的各环节进行仿真，能够实现相位的跟踪(It mainly simulates all links of PLL, and can realize phase tracking)

说明：  isight集成ABAQUS、Adams、MATLAB等软件的源码(isight integration with ABAQUS Adams MATLAB Program)

可以实现一些高精度，超出CPU所能一次运算的数，实现任意数值长度的运算。-Can achieve a number of high-precision, beyond the CPU can be a number of operations, realization of arbitrary length numerical computing.

ARM download when you need.

floating point divider

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,

一个小的文件分割器。-a small file dividers.