Image_Segmentation_Patern_Recognition_Beta_Versio

说明：  MATLAB编程教材，从基础讲起，让你一步步进入到MATLAB的世界~(matlab programing tutorial)

说明：  slipping model control (vc)

遗传算法求解tsp包括论文和源程序以及必要说明(MATLAB tsp )

用于图像精确匹配以及目标检测的SIFT算法(Sift algorithm matlab version for object detection and image match)

相位解缠和相位展开matlab程序，，里面还包含多篇介绍该相关解缠算法的文章 (Phase unwrapping and phase to commence the matlab program, which also contains many articles on the unwrapping algorithm article)

这是关于微分方程求解的资料，我自己看了好几遍，很经典，并且也很全面，并附有例子(This is a differential equation solver, I have read it several times, very classic, and also very comprehensive, with examples)

PC matlab memory check routine. :)

Practical Experiment for Discharging a capacitor in 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.)

image enhancement in time domain and frequency domain