CDMA

在判别分析中使用粒子群优化算法求解最优判别函数(Particle Swarm Optimization(PSO))

通过matlab代码，小波包分析提取振动信号中的特征频率，可以得到很精确的幅值、频率、相位估计。( By matlab code, Wavelet packet analysis to extract vibration signal characteristic frequency, You can get a very accurate amplitude, frequency, phase estimation.)

least mean square algorithm for estimation state

形态学操作，图像的膨胀和腐蚀操作，使用matlab进行实现。(Morphological operations, image dilation and erosion operations, using matlab to achieve.)

"1D FDTD solution for open boundary in a unitless domain with a Courant factor > 1 (Unstable solutions leading to blow-up)"

下垂控制的matlab仿真,有功和无功的调节。(simulink of droop control，adjust of active power and reactive power.)

使用模糊系统方法来实现二维函数的逼近，效果很好，matlab编程仿真(fuzzy systems use methods to achieve the two-dimensional function approximation and the results very good, Matlab Simulation Programming)

采用多天线的认知无线电系统相对于单天线认知无线电系统，既有传统的资源（频域，时域，码域），又有空域资源。本文研究基于博弈论的有限反馈认知MIMO系统的资源分配，涉及发射功率、空域（采用波束成形）和反馈速率等资源的分配。利用有限的反馈信道反馈量化的信道状态信息(CSI)，基于量化CSI，本文主要研究联合功率分配与波束成形优化、次用户（认知用户或非授权用户）反馈速率控制、联合功率分配与反馈速率控制等相关资源分配问题。(Multi-antenna cognitive radio system with respect to a single antenna cognitive radio systems, both traditional resource (frequency domain, time domain, code domain), there airspace resources. In this paper, a limited resource allocation game theory of cognitive MIMO system based on feedback, involving the launch distribution power, airspace (using beam-forming) and feedback rate and other resources. With limited feedback channel feedback quantized channel state information (CSI), based on quantified CSI, this paper studies the joint power allocation and beamforming optimization, secondary users (cognitive user or unauthorized user) feedback rate control, joint power allocation and feedback rate control and other issues related to resource allocation.)

采用前推回代法计算配电网潮流的程序，同时给出了IEEE33节点的算例(Using forward and backward substitution method to calculate distribution power flow program, and gives examples of nodes IEEE33)

贝叶斯学习算法The inference model based on Bayesian Network is discussed and the algorithm based on the model is presented.(The inference model based on Bayesian Network is discussed and the algorithm based on the model is presented.)