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卷积码编码译码MATLAB仿真程序，比较详细的源代码(Convolutional coding and decoding MATLAB simulation program)

matlab利用符号工具箱进行解析函数求导演示的编程源码及示例图片(matlab symbolic toolbox analytic function to ask the director to show programming source code and sample pictures)

本源程序是基于Newton迭代改进的Steffensen加速法，具有更好的收敛性质(The source code is based on the Newton iteration acceleration method to improve the Steffensen, has better convergence properties )

利用MATLAB实现遗传算法对非线性问题的求解，进行迭代计算(Genetic algorithms using MATLAB for solving nonlinear problems, iterative calculation)

说明：  读取sgy格式数据，并利用wigb进行成图(Read sgy format data, and use wigb to map)

heat conversion and fare calculator for simulink

晶闸管六脉波整流,用与大功率电源谐波分析(six pulse converter)

说明：  解两点边值问题的有限元程序代码（matlab）(Solution of Boundary Value Problems of Finite Element code (matlab))

一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。 (A new approach to compare models for body area networks (BAN) that accommodates multiple links and multiple subjects is presented. The absolute measure described allows comparison across a spectrum of possible characterizations ranging from single-parameter for an entire ensemble, through to per-activity, per-subject and per-link based parameterized models. The use of an explicit trade-off between error and complexity, combined in a goodness-of-fit measure, is shown to have important consequences when applied to a range of typical BAN channel data. It is shown that there are different implications in choice of model, and it’s associated complexity, for mixed-activity “everyday” data, when compared with set-activity dynamic data (e.g. walking). The deficiency of mean path loss, or even median path loss measures, as a sole characterization of the BAN channel is also highlighted.)

对数据进行按列分类统计，统计平均值、背景值、方差等(The data by columns classification statistics, statistical average, the background value, variance, etc.)