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最终的权值矩阵就是滤波器的系数，包含了阵列信号处理的常见算法，是一种双隐层反向传播神经网络，相控阵天线的方向图（切比雪夫加权），添加噪声处理。( The final weight matrix is ??the filter coefficient, Contains a common array signal processing algorithm, Is a two hidden layer back propagation neural network, Phased array antenna pattern (Chebyshev weights), Add noise processing.)

说明：  研究能量受限的SWIPT无线通信网络中，基站采用MIMO多天线发送信号给用户节点，在有窃听节点的情况下，通过联合优化发射波束形成矢量、人工噪声协方差和接收PS比来最小化总发射功率，同时保证每个接收机的个体保密率和能量收集约束。(In the energy limited swipt wireless communication network, the base station uses MIMO multi antenna to send signals to user nodes. In the case of eavesdropping nodes, the total transmit power is minimized by jointly optimizing transmit beamforming vector, artificial noise covariance and receive PS ratio, while ensuring the individual confidentiality rate and energy collection constraint of each receiver.)

图像频域变换源代码，只针对该应用领域内的同行用于学术研究，不能用于商业活动！！(Image Transferation original codes)

利用MLC来估计GMM 模型参数，压缩文件没有加密可以直接使用(use MLC to estimate model parameters GMM, compressed files directly without the use of encryption)

利用m序列生成跳频序列并用于跳频通信，并给出误码率曲线。(M sequence to generate the frequency hopping sequence and used for frequency hopping communication, and gives the BER curves)

通过matlab仿真软件对有源电力滤波器进行了仿真，利用的无功功率理论对谐波进行检测(Active power filter simulation matlab simulation software, the use of reactive power theory of harmonic detection)

外国一个大学生做了一个精确定位系统，这个系统几乎可以跟踪镜头里的任何物体。只要你能看见它，并把它选中。有兴趣的同学可以下载我传上来的源文件...是用matlab写的...demo文件也在里面 (A foreign university students made a precise positioning system that can track almost any object lens. As long as you can see it, and to select it. Interested students can download the source files I pass up ... with matlab ... demo files are written inside)

独立元分析 ICA matlab程序 可以参考下(ICA ICA matlab program can refer to the following)

misosimu simulates the ser for different miso systemms 2*1,3*1 4*1 and 8*1

在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。( In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection, a novel method of network anomaly detection based on constructing wavelet neural network (WNN) using quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The WNN was trained by QPSO. )