mysusan

利用蚂蚁算法求解最短路径问题，稍作修改既可以用于移动机器人路径规划当中。(Ants use the shortest path algorithm to solve the problem, with some slight modifications can be used in mobile robot path planning among them.)

OF,实际上OF是MCM Multi-CarrierModulation，多载波调制的一种。其主要思想是：将信道分成若干正交子信道，将高速数据信号转换成并行的低速子数据流，调制到在每个子信道上进行传输。正交信号可以通过在接收端采用相关技术来分开，这样可以减少子信道之间的相互干扰 ICI 。每个子信道上的信号带宽小于信道的相关带宽，因此每个子信道上的可以看成平坦性衰落，从而可以消除符号间干扰。而且由于每个子信道的带宽仅仅是原信道带宽的一小部分，信道均衡变得相对容易。在向B3G/4G演进的过程中，OFDM是关键的技术之一，可以结合分集，时空编码，干扰和信道间干扰抑制以及智能天线技术，最大限度的提高了系统性能。(OF)

各种matlab经典算法，在此整体，以便快速查找引用(various classical algorithm, in this whole, in order to use Quick Search)

数字波束形成算法库 包括6种数字波束形成算法的matlab源程序(digital beamforming algorithms library, including six species of digital beamforming algorithms Matlab source)

第k小元素（分治法） 给定一个线性序列集，要求求出其中指定的第K小的数的值和位置.如给定n个元素和一个整数i，1≤i≤n，输出这n个元素中第i小元素的值及其位置；(First k small element (divide and conquer) Given a set of linear sequence, where the specified requirements determined the K value and the small number of locations, such as a given n elements and an integer i, 1 ≤ i ≤ n, output this is the n elements of the i-th element value and the position of the small )

适合初学者从入门到精通matlab编程所用，带有十三个部分的源代码，层层递进，个人感觉不错！(Suitable for beginners matlab programming from entry to the proficient use, with thirteen parts of the source code, progressive layers, personal feeling good!)

Matlab代码实现了一个调制的GUI界面，同时实现了ASK, OOK, BPSK, QPSK, 8PSK调制算法的源代码。以图像形式展示了各种调制算法的实际运行结果。 (Matlab code implementation of a modulation GUI interface, at the same time achieve the ASK, OOK, BPSK, QPSK, 8PSK modulation algorithm source code. Image form a variety of modulation algorithm to display the actual results.)

SQPLab program in Matlab

单极性H桥DC-DC逆变电路仿真，结果正确，输出波形好(Unipolar H-bridge DC-DC inverter circuit simulation, the result is correct, the output waveform)

此为一篇介绍mcmc方法的外文文献 非常适合刚刚接触mcmc方法的初学者(This paper introduces the readers of the Proceedings to an important class of computer based simulation techniques known as Markov chain Monte Carlo (MCMC) methods. General properties characterizing these methods will be discussed, but the main emphasis will be placed on one MCMC method known as the Gibbs sampler. The Gibbs sampler permits one to simulate realizations from complicated stochastic models in high dimensions by making use of the model’s associated full conditional distributions, which will generally have a much simpler and more manageable form. In its most extreme version, the Gibbs sampler reduces the analysis of a complicated multivariate stochastic model to the consideration of that model’s associated univariate full conditional distributions.)