dpdk-release-notes-1.7.0

逆变器仿真模型，三电平SVPWM调制，分享给需要的朋友(Simulation model of the inverter, three-level SVPWM modulation, share with the needy friend)

阵列信号处理里面的经典算法，music算法，详细解答(Array signal processing algorithms inside classics, music algorithm, detailed answers)

由四个智能体组成的系统强连通拓扑结构，在无结构不确定性的条件下，系统一致性的仿真代码。(Made up of four agent system strongly connected topology, in the absence of structural uncertainty, the consistency of system simulation code.)

This demo explains the use of the advanced object oriented interface model. The model is the generic object which collects all relevant signals, parameters, options and functions related to an application of the LS-SVM. This interface is depreciated for the casional users because of the implicit nature: the distinction between in- and output vanishes. If one wants to use the full power of LS-SVMlab, one is recommended to go through this demo. The options can be divided in 5 classes, the general LS-SVM options the trainpoint administration, the options of the Conjugate Gradient algorithm the preprocess options and the encoding options. The demo shows how to control the preprocessing and the coding using the appropriate model. It is adviced to check carefully the model s options before starting the calculations.

这是一本关于内核源代码分析的好书，希望能给大家带来益处(This is a core source code analysis on the books, we hope that they will benefit)

卡尔曼和维纳滤波器的设计及使用 附带源代码(Kalman and Wiener filter design and the use of collateral source code)

钟麟《Matlab仿真技术与应用教程》学习用(Zhong Lin Matlab Simulation Technology and Application Guide)

All of a three-phase inverter bridge circuit simulation The circuit is simulated in a full-bridge inverter three phase IGBT is offered with a six and six diode.

it s a stepper motor control

标准压缩感知（CS）理论决定了可靠的信号恢复是可能给M= O（KLOG（N / K））的测量。我们证明了它可以通过利用超越简单的稀疏性和可压缩性由包括价值观和信号系数的位置之间的依赖关系更加逼真信号模型大大降低Mwithout牺牲的鲁棒性。(The standard compressive sensing (CS) theory dictates that robust signal recovery is  possible from  M=O(Klog(N/K))  measurements. We demonstrate that it is possible to substantially  decrease  Mwithout sacrificing robustness by leveraging more  realistic signal models that go beyond simple sparsity and  compressibility by including dependencies between values and  locations of the signal coefficients.   )