图选项卡jQuery焦点图

基于STC15F开发的航模启动起落架低气压保护程序(Low pressure protection program for landing gear starting and landing gear based on STC15F)

华中科技大学版的电力系统分析，非常好的一本适合电气工程专业的书籍。(Huazhong University of Science and Technology power system analysis, a very good book for electrical engineering.)

此程序是进行2ask调制解调，解调采用非相干解调方式(Fask modulation and demodulation)

说明：  用于计算Mie散射的成像：包括散射，消光以及吸收系数。建议MATLAB在2014版下运行。(Mie scattering calculation of scattering extinction and absorption.)

SVR源码以及详细交通流预测模型，里面有SVM，SVR，LSSVM(SVR source code and detailed traffic flow prediction model, including SVM, SVR, LSSVM)

实现物理层安全，通过加入干扰噪声，提高系统容量。(The physical layer security is realized in this paper. By adding interference noise, the system capacity is improved.)

测试信号源 labview程序-Labview signal source testing procedures. . . . . . . . . . . . . . .

说明：  Python环境下 愤怒的小鸟 强化学习程序完整版，Qlearning 算法(The full version of Angry Birds Reinforcement Learning Program in Python Environment, Qlearning Algorithm)

net Ajax无刷新技术的使用说明 及 相关的动态数据库。-net Ajax without the use of technology refresh and the related dynamic database.

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,