yearchixufuhe

异步电动机直接转矩控制模型，simulink仿真模型，改进后的直接转矩控制(Asynchronous motor direct torque control model, simulink simulation model, the improved direct torque control)

guassian filtering used in DIP

Crout Algorithm for Power Flow

说明：  采用均值聚类的方法找出径向基函数中心，并对训练数据展开预测(Mean clustering method is used to find the center of radial basis function and forecast the training data.)

说明：  基于小波变换的多尺度图像边缘检测matlab源代码(在Matlab7.0下运行) (Based on wavelet transform multi-scale edge detection matlab source code (at run Matlab7.0))

说明：  matlab基础源程序，适合matlab初学者！(matlab basic source for matlab beginners!)

matlab最优化程序在数学建模中的应用教程(matlab optimization program in the application of mathematical modeling tutorial)

检测估计和调制理论 迄今为止，本书仍然是公认的关于检测、参数估计和线性与非线性调制波形估计方面的一本论述详尽、深入、成功的著作。也是一本较好的研究生教材。内容是研究发生在通信、雷达和声呐中被噪声污染了的各种线性与非线性调制、随机与非随机波形的最佳处理的数学方法和物理结构，各种系统可能达到的性能极限和影响它的各种因素。全书包括导论、经典检测和估计理论、随机过程的描述、信号检测与信号参数估计、连续波估计、线性估计和讨论等7章。每章附有相当数量的扩大内容范围的习题。 (Detection Estimation and Modulation Theory So far, the book is still recognized on the detection, parameter estimation and estimation of linear and nonlinear modulation waveform s a detailed discussion, thorough and successful work. Is also a good post-graduate teaching. Content is research in communications, radar and sonar noise pollution has been a variety of linear and nonlinear modulated, random and non-random waveforms of the best methods of dealing with the mathematical and physical structure of the performance of the various systems may reach the limit and the various factors affecting it. The book includes introduction, classical detection and estimation theory, stochastic process description, signal detection and signal parameter estimation, continuous-wave estimation, linear estimation and discussion of Chapter 7. Each chapter is accompanied by a considerable number of exercises the expansion of content areas.)

Risk estimation for wireless networks

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)