reconstruction-of-source-signals

这个工具是同伴的图书软件的复杂性管理的模糊系统 ，斯普林格，2010。 一个新的规则为基础的系统类型，称为规则为基础的网络，已与行动建议操作规则库的互联网络，形成一个等效的单规则库。这些行动最近已实现为基于Matlab工具箱，但仅限于个人使用的节点对。此工具自动执行正确网络中的所有节点的基本操作。该工具会让你更容易实现用尽可能少的用户交互的各种规模的规则为基础的网络 (This tool is companion software for the book Complexity Management in Fuzzy Systems , Springer, 2010. A novel type of rule based system, called a rule based network, has been suggested with operations to manipulate networks of interconnected rule bases to form an equivalent single rule base. These operations have recently been implemented as a Matlab based toolbox, but are limited to individual use on pairs of nodes. This tool automatically performs the basic operations correctly on all nodes in a network. The tool will make it much simpler to implement rule based networks of all sizes with as little user interaction as possible. )

用matlab仿真，用PID控制实现调速(matlab，PID controller)

关于无线定位解算的算法，包括TDOA、AOA解算过程。(About Wireless Location solver algorithms, including TDOA, AOA solving process.)

人脸定位代码，短小但精度和速度都很高，matlab版本不能太低(Face location code, a short but very high accuracy and speed,)

只是对数字预失真器的修复模型，基于对自适应处理延迟效果不佳问题，提出的改善方法。(Only the digital predistorter repair model, based on an ineffective adaptive processing delay problems and improve the method proposed.)

A program to calculate the flight path of a projectile (e.g., mortar shell) using the standard equations of motion.

matlab基础编程介绍，对于初学者有用(matlab basic programming introduction, helpful for beginners)

Advanced steganography using matlab

This simulink model contains the complete description of building PV MODEL OF BPSX150S power generation scheme.

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.)