vitebi_decode_wy

k-means 源代码 k-means is a very useful hierarchical clustering in meachine learning (k-means is a very useful hierarchical clustering in meachine learning )

search in list box in exe form

通过粒子滤波来跟踪目标 可以在vs2008下直接运行(Through the particle filter to track the targets can be run directly under vs2008)

以前使用matlab和vc混合编程都使用的是先将m程序用mcc转化成dll，再在vc中调用 的方法。这种方法对比较复杂的m文件很方便，但是对一些小的程序，如曲线可视化、计算矩阵特征值等，对这些只需要一两行代码的小程序，再使用dll就得不偿失了。经过试验后，发现可以直接在C++程序中调用matlab c/c++ math & graphic library的函数。(before using Matlab and vc mixed programming using the first procedure used mcc m into dl l, then vc call method. This method of complex documents m very easy, but for some minor procedures, such as curve visualization, matrix eigenvalue calculation, and so on, these are just two lines need a small procedure code, reuse dll on the loss outweighs the gain. After testing, can be found directly in the C Programming Calling Matlab c/c math)

這是一個C#框架, 實現了,很smart clien功能(This is a C# framework to achieve, and it's very functional smart clien)

Mean Shift 这个概念最早是由Fukunaga等人[1]于1975年在一篇关于概率密度梯度函数的估计中提出来的,其最初含义正如其名,就是偏移的均值向量,在这里Mean Shift是一个名词,它指代的是一个向量,但随着Mean Shift理论的发展,Mean Shift的含义也发生了变化,如果我们说Mean Shift算法,一般是指一个迭代的步骤,即先算出当前点的偏移均值,移动该点到其偏移均值,然后以此为新的起始点,继续移动,直到满足一定的条件结束. 用matlab实现mean shift算法仿真(Mean Shift the concept was first used by Fukunaga et al [1] in 1975 in an article on the probability density function estimation of gradient raised its original meaning, as its name implies, is the offset of the mean vector, where is the Mean Shift a noun, it refers to a vector, but with the Mean Shift the development of the theory, Mean Shift the meaning has changed, if we say that Mean Shift algorithm, generally refers to an iterative steps, namely, first calculate the current point Mean offset, move the shift points to its average, and then as a new starting point and continue to move until the end to meet certain conditions. realize using mean shift algorithm matlab simulation)

隐马尔科夫模型相关算法，包括前向算法、后向算法，维特比算法等(Hidden markov model algorithm, including the forward algorithm and backward algorithm and viterbi algorithm, etc)

matlab processing and realization of the voice, very useful

智能天线技术是第三代移动通信系统的关键技术之一，也是国内外热门的研究课题。由于无线移动通信的信道传输环境具有复杂性和不确定性，存在多径衰落和时延扩展，因此造成了符号间串扰、同信道干扰、多址干扰等，这些干扰降低了链路性能和系统容量，智能天线技术是解决以上问题的方法之一。 本文首先阐述了智能天线和白适应波束形成的基本理论，然后对自适应算法进行了研究。对一些基本的自适应算法最小均方算法、恒模算法及递推最小均方算法进行了分析讨论，用计算机仿真的结果论证了算法的性能。针对相干干扰介绍了空间平滑技术，对传统的相干干扰抑制方法-空间平滑法进行了分析，采用改进算法有效降低了空间平滑算法的运算量。对信号波达方向(DOA)的估计是智能天线研究的另一关键问题。第四章在介绍了阵列信号处理方法的基础上，先简单介绍了Bartlett、Capon、MUSIC等空间谱估计算法，分析了信源数目过估计与欠估计情况下的MUSIC算法性能，提出一种改进算法，在信源数目有误差的情况下该算法的性能良好。第五章针对多径干扰环境，在分析MMUSIC算法的基础上，本文提出了基于低秩矩阵逼近法的改进MUSIC算法(In this paper, firstly, the author illustrates the principles of smart antenna and adaptive beam forming, and then introduces some typical adaptive algorithms, the LMS ,CMA and RLS algorithms are analyzed and discussed and simulate them in the computer to prove their capability. but they have the disadvantage of high complexity, so they are not fit for practice. The paper analyses the conventional method rejecting spatial smoothing coherent interfering signals, which is spatial smoothing technique)

近代平差，含有近代经典平差函数模型，适用于数据处理中的近代平差应用(Modern adjustment, containing a model of modern classical adjustment function for data processing applications in modern Adjustment)