mimobpsk

matlab based MMZ codes for wireless system

detection and localisation of objects

输入某一点的应力值（6个），可以求出其主应力、主应力方向以及切应力值(Enter a point of stress (6), can find the principal stresses, principal stress direction and the shear stress)

这个是自适应卡尔曼滤波的程序，可以不用已知观测方程以及状态方程的噪声，能够在滤波时学习出(This is an adaptive Kalman filtering program that can do the known equations of state and noise measurement equation can learn while filtering out)

MATLAB 阻抗计算例子1，很有参考价值。。(MATLAB impedance calculation example 1, a good reference. .)

说明：  mat lab designed filter

模型预测控制是20世纪80年代初开始发展起来的一类新型计算机控制算法。该算法直接产生于工业过程控制的实际应用，并在与工业应用的紧密结合中不断完善和成熟(Model predictive control is a new class of computer control algorithms developed in the early 1980s . The algorithm directly to produce practical applications in industrial process control , and continue to improve and mature closely integrated with industrial applications)

MATLAB 编程与系统仿真(MATLAB programming)

说明：  压缩感知 正交匹配追踪一些人关心压缩感知与雷达成像，他们把稀疏表示放在最重要的地方，以为在雷达成像中成功实现压缩感知关键是稀疏表示； 事实上并不是如此。我们知道：压缩感知需要建立AX=B,且该方法具有较低的抑制信噪比能力；另外雷达成像的基础是雷达 信号与目标的相互作用，也就是电磁波与介质的相互作用，该相互作用是一个非常复杂的非线性问题，因此研究这个问题与 压缩感知的关系才是解决雷达成像问题的关键点所在。从另外一个角度来看，雷达成像中惯用的方法是匹配滤波，它之所以 能够处理低信噪比的问题，是因为它利用了回波数据的冗余信息。也就是目前雷达成像算法之所以成功的关键是具有足够 多的冗余信息。现在，在雷达成像中使用压缩感知恰好是反其道而行之！ (compressed sensing)

In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification. Although different classification models have been developed for network intrusion detection, each of them has its strengths and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.