DSG2

Wireless Communication Engineering Modulation-Analog Modulation: AM Radio-FM Radio-Detection of FM Signal-Digital Modulation-Digital Demodulation-Coherent Versus Noncoherent-Amplitude Shift Keying (ASK)-ASK Modulation-Baseband Filtering-Non–Coherent ASK Detection -FSK Generation

说明：  关于亚像素边缘检测的一些文章，感觉比较有用(Subpixel edge detection on some articles, I feel more useful)

说明：  OFDM的simuink仿真，可以指导你怎样搭建OFDM通信系统的框架。(OFDM simuink simulation can guide you how to build OFDM systems framework.)

有关cognitive radio的内容,对matlab进行的模拟测试,希望对大家有所帮助(the cognitive radio the content of the Matlab simulation tests We hope to help)

Matlab for Heat transfer

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )

generation of rectangular shape signal. for a rectangular shape of width 1, sinc result also included

说明：  EMD程序包，处理信号的时频分析算法，包含希尔伯特变化程序。(EMD procedure)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)

伺服电机控制系统，02年开始积累，效果好，速度快，PI控制，经典控制，fuzzy PID，自适应控制，速度位置控制等等，欢迎大家参考。(Servo motor control system, in 2002 began to accumulate, effective, fast, PI control, classical control, fuzzy PID, adaptive control, speed position control, etc., we welcome the reference.)