LSA

matlab插值，详细介绍了MATLAB在插值已及实验数据处理方面的应用(matlab interpolation, described in detail in MATLAB and experimental data have been interpolated deal with the application)

Code to obtain the filtering function polynomials F(s), E(s) and P(s), and the constants eps and eps_r from transmission zeros, with Cameron s method.

包含在word中提到的大部分同步算法，包括时间和频率同步(It contains most of the synchronization algorithm in the word mentioned, including time and frequency synchronization)

经典的国外bitshop关于机器学习和模式识别教程，全是英文，内容详细(Classic foreign bitshop tutorial on machine learning and pattern recognition, all in English, detailed)

个时频原子的模糊函数及理想时频分布；三个时频原子的理想时频分布 (In time-frequency atom ambiguity function and the ideal time-frequency distribution three time-frequency atom ideal time-frequency distribution)

说明：  内维尔插值在很多学科都能用到，相信对大家有用！(Neville interpolation can be used in many disciplines, I believe it would be useful!)

分析了BFSK的性能及原理，并利用matlab仿真实现其误码率等信息。写成word文档，后面附有源程序。(BFSK performance analysis and principle, and use matlab simulation to achieve its bit error rate information. Word document written in the back with a source.)

matlab porgram for license plate

hamming 编码，线性分组码，信道编码(hamming coding, linear block codes, channel coding)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)