高通量低成本实现对spartan3E FPGA AES-128算法

摘要:本文旨在研究一种能对复杂热工对象的有效建模方法。基于遗传算法的辨识方法有较强的抗干扰能力,对低、高阶系统、延时系统都可以达到很好的辨识效果。根据单元机组的低阶非线性模型,推导出一个双进双出、能够描述机组动态特性及机炉间相互耦合关系的协调控制系统传递函数矩阵。依次模型为基础,提出一种基于改进的遗传算法的参数辨识方法。-Abstract: This paper aims to study a thermal complex objects can be an effective modeling method. Identification method based on genetic algorithm has strong anti-jamming ability, low, high-end systems, delay systems can achieve very good recognition results. According to unit power plant low-level non-linear model, derived a double inlet and outlet, can describe the dynamic characteristics and boiler-turbine unit is coupled between two relations, coordination and control system transfer function matrix. Turn model was proposed based on improved genetic algorithm based on parameter identification method.

自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are: - Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, - Vector Autoregressive modeling and Detection [uses ARMASA] - Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum. - ARfil algorithm: The analysis of missing data/irregularly sampled signals - Subband analysis: Accurate analysis of a part of the power spectrum - Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio. - Analysis of segments of data, possibly of unequal length.

财经类数值算法(finacial numerical recipes),压缩包内有pdf文件和c++源码-Financial category numerical algorithm (Financial numerical recipes), compressed within a pdf file and c-source

利用opencv实现JPEG图像压缩，并进行编码解码，此外本程序还有一个滚动条，方便读者主观评价图像质量

C语言源代码 万年历-C language source code hefty almanac

FIR数字滤波器的设计，C语言实现，可以直接运行

为实现使用哈希技术的直接访问文件的程序。碰撞处理采用线性探测和链接没有更换。[散列函数 = (record_id mod 10)]

列主元的高斯算法-out of the PCA algorithm Gaussian

著名的迭代就近点算法（icp）的源代码，在20世纪80年代中期，很多学者开始对点集数据的配准进行了大量研究。1987年，Horn[1]、Arun[2]等人用四元数法提出点集对点集配准方法。这种点集与点集坐标系匹配算法通过实践证明是一个解决复杂配准问题的关键方法。1992年，计算机视觉研究者Besl和Mckay[3]介绍了一种高层次的基于自由形态曲面的配准方法，也称为迭代就近点法ICP（Iterative Closest Point）。以点集对点集（PSTPS）配准方法为基础，他们阐述了一种曲面拟合算法，该算法是基于四元数的点集到点集配准方法。从测量点集中确定其对应的就近点点集后，运用Faugera和Hebert提出的方法计算新的就近点点集。用该方法进行迭代计算，直到残差平方和所构成的目标函数值不变，结束迭代过程。ICP配准法主要用于解决基于自由形态曲面的配准问题。

svm实现的例子-SVM realize examples