clpm_7.8

说明：   本书介绍了在Microsoft Windows 98、Microsoft Windows NT 4.0和Windows NT 5.0下程序写作的方法。这些程序用C语言编写并使用原始的Windows Application Programming Interface（API）。如在本章稍后所讨论的，这不是写作Windows程序的唯一方法。然而，无论最终您使用什么方式写作程序，了解Windows API都是非常重要的。(This book describes the Microsoft Windows 98, Microsoft Windows NT 4.0 and Windows NT 5.0 under the process writing approach. These procedures used C language and use the original Windows Application Programming Interface (API). As discussed later in this chapter, and it is not writing the only way to Windows program. However, regardless of how you use the writing process, understand the Windows API are very important.)

说明：  求光斑中心点坐标,对图像二值化。并且对其中心坐标求出(mean)

一维TM模的PML边界设置......................(PML(perfect Match Layer) Boundary Condition of 1D TM mode...)

均衡技术的matlab仿真。包括时域均衡和频域均衡后的信噪比对比(Equalization matlab simulation. Including time domain equalization and frequency domain equalization SNR comparison)

在经典波达角估计方法多重信号分类算法中提出了改进(Angle of arrival estimation method in the classical Multiple Signal Classification algorithm in the proposed improvements)

blood vessel extraction

进行LTE 信道估计仿真器，对LTE进行信道估计，在LTE系统中是一个很重要的部分，是做链路级仿真必要步骤。(LTE channel estimater)

数值分析中基于Matlab环境下的Gauss全主元消去法的源代码(Numerical analysis in Matlab environment based on principal component elimination method Gauss full source code)

书籍——[MATLAB从入门到精通].周建兴.清晰版.pdf(Books- [MATLAB from entry to the master]. Zhou Jianxing clear. Pdf)

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)