communications-sim

mppt_incc is incremental conductance

基于有限元理论，通过matlab编程求解悬臂梁受力问题(Based on finite element theory, matlab programming by solving problems cantilever force)

系统讲述了matlab在数学建模中的应用，有助于良好的掌握matlab的工具箱使用和进一步了解matlab的强大功能。(Matlab system described in the application of mathematical modeling to help a good grasp of the matlab toolbox to learn more about the use and power of matlab.)

Adaptive Schemes for Expanded Diagnosis Image Evaluation for skin cancer Skin cancer is a malignant tumour which grows in skin cells. It is one of the most common of all cancer which affects human beings and accounts for more than 50 of all types of cancers around the world. Skin cancer is skin’s unwanted growth with differing causes and varying degrees of malignancies. It can spread very fast to all organs/parts of human body through lymphatic system or blood.

振动理论中的主动控制程序，对于振动方面的学习有一定的帮助。(The active control of vibration theory program, the vibration aspects of learning have some help.)

详细说明了有关变速不变调算法的基本原理以及在MATLAB上的实现(Detailed description of the basic principles relating to the same transfer speed algorithms and implemented on the MATLAB)

利用MATlab进行混合溶液沸点的计算，里面包含的模型自己参照，值得学习和推荐。(Mixed solution using MATlab boiling point calculation, which contains its own reference model, it is worth learning and recommendations.)

Calculate Embed a Time Series and calculate the Largest Lyapunov exponent and the Correlation Dimension as well as usefull plot. Direct Method, Rosemstein and Grassberger-Procaccia algorithm.

sliding mode controle

ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model see help icp for more information (ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model- matrix with model points, data- matrix with data points, OUTPUT: R- rotation matrix and T- translation vector accordingly so newdata = R*data+ T . newdata are transformed data points to fit model see help icp for more information )