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无线传感器网络里的定位经典dvhop算法实现(Wireless sensor networks where the positioning of dvhop Algorithm)

Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds

The purpose of this Primer is to help you begin to use MATLAB. It is not intended to be a substitute for the online help facility or the MATLAB documentation (such as Getting Started with MATLAB, available in printed form and online). The Primer can best be used hands-on. You are encouraged to work at the computer as you read the Primer and freely experiment with the examples. This Primer, along with the online help facility, usually suffices for students in a class requiring the use of MATLAB.

Since the first edition of this book was published seven years ago, the field of modeling and simulation of communication systems has grown and matured in many ways, and the use of simulation as a day-to-day tool is now even more common practice. With the current interest in digital mobile communications, a primary area of application of modeling and simulation is now in wireless systems of a different flavor from the `traditional ones. This second edition represents a substantial revision of the first, partly to accommodate the new applications that have arisen. New chapters include material on modeling and simulation of nonlinear systems, with a complementary section on related measurement techniques, channel modeling and three new case studies a consolidated set of problems is provided at the end of the book.

灰度共生矩阵和灰度梯度共生矩阵的提取方式，是比较重要的纹理特征提取方法，用matlab实现的(The GLCM and gray gradient co-occurrence matrix extracted, texture feature extraction method, using matlab to achieve)

基于matlab神经网络的文字识别 有详细注释(Matlab neural network-based character recognition)

matlab EMD 降噪方法 m文件 可以分析振动信号(Analysis of vibration signal can be matlab EMD noise reduction method for M documents)

Matlab implementation of Induction motor control and converter implementation

隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)

智能天线 自适应非盲算法LMS。用MATLAB编写而成，第三部分(Non-blind algorithm for smart antenna adaptive LMS. Compiled with MATLAB, the third part)