LMSalgohm

说明：  用matlab做的一个电气工程课程设计界面，其中包含接入系统设计，主变压器经济比较，牛拉法解潮流及阻抗法解短路故障(包含不对称故障)(A GUI created by matlab for design class of Electrical Engineering.I hope it can help everyone who need this kind of thing. )

It is about matlab which depicts brain image segmentation

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and
1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

語音信號是基於時間軸上的一維數位信號，在這裏主要是對語音信號進行頻域上的分析。在信號分析中，頻域往往包含了更多的資訊。對於頻域來說，大概有8種波形可以讓我們分析：矩形方波，鋸齒波，梯形波，臨界阻尼指數脈衝波形，三角波，餘旋波，余旋平方波，高斯波。對於各種波形，我們都可以用一種方法來分析，就是傅立葉變換：將時域的波形轉化到頻域來分析。(Voice signal is based on the timeline of one-dimensional digital signal, where mainly the speech signal in frequency domain analysis. In signal analysis, frequency domain often contains more information. For the frequency domain for about 8 allows us to analyze waveforms: rectangular square wave, sawtooth, trapezoidal wave, the critical damping index pulse, triangle wave, I spin wave, I spin square wave, Gaussian. For all waveforms, we can analyze in a way that is Fourier transform: the transformation of time domain waveform into the frequency domain to analyze)

barker码产生的源程序，用matlab编写而成(err)

amplitude modulation(AM) and DSB-SC source code

这是一个实现MCMA+DD算法的盲均衡算法，在已有的CMA算法基础上进行改进，可以解决相位旋转(This is a realization MCMA+DD algorithm blind equalization algorithm, the existing CMA algorithm based on improved phase rotation can be solved)

2010版本matlab教程，非常的清晰，内容全面，适合需要学习的人士。(2010 version of the matlab tutorial, very clear, comprehensive, suitable for those who need to learn.)

Model Based Steganography Source code by Matlab

核查的关键在于对核材料属性准确、有效地识别[1][2]。利用252Cf作为随机脉冲中子源，对其入射核材料所产生的诱发裂变中子脉冲信号进行采集、处理和分析，得到自相关、互相关、自/互功率谱、功率谱密度比等一系列参数，从而反演核材料内部的反应性的情况，便可用于分析和识别核材料，其基本原理就是252Cf中子源驱动噪声分析测量法[(The result shows that the trained Elman neural network is able to identify the characteristics of correlation function, to distinguish different concentrations, and the average recognition rate reaches 90 , with high robustness.)