MLS1DShape

一维光子晶体的传输特性和它结构参数的matlab程序。(One-dimensional photonic crystal structure of the transmission characteristics and parameters of its matlab program.)

matlab 的拟牛顿方法，减少计算导数带来的计算量的一种迭代方法，用来计算非线性问题。(matlab quasi-Newton methods to reduce the computational calculation of the amount of the derivative of an iterative method to calculate the nonlinear problem.)

一篇关于高精度的，对声源定位的的方法介绍(_A High-Accuracy, Low-Latency Technique for Talker Localization in Reverberant Environments Using Microphone Arrays----SRP-PHAT)

Pulse generation of inverters

对七号信令MAP协议进行非常细致的讲解，大家有需要的可以下载参考(MAP signaling protocol on the 7th a very detailed explanation, you need to download the reference)

设计巴特沃斯和切比雪夫滤波器，实现低通滤波，并计算滤波系数，给出效果图(Design a 20-order Butterworth filter, low-pass filter, and the filter coefficient calculation gives renderings)

说明：  ＧＰＳ星座ＭＡＴＬＡＢ仿真 ＧＰＳ星座ＭＡＴＬＡＢ仿真(MATLAB Simulation of GPS constellation GPS constellation MATLAB simulation)

说明：  实现bpsk在AWGN下的调制与解调，误码率计算。(psk in Rayleigh fading channel simulation model. Simulink achieve. Simulation can see Rayleigh fading channels for BPSK performance impact. And to be compared Rayleigh Channel and AWGN under BPSK performance)

FOUNDATIONS OF FUZZY CONTROL A PRACTICAL APPROACH, Jan Jantzen, 2nd, 2013

盲源分离（BSS: Blind Source Separation），又称为盲信号分离，是指在信号的理论模型和源信号无法精确获知的情况下，如何从混迭信号（观测信号）中分离出各源信号的过程。盲源分离和盲辨识是盲信号处理的两大类型。盲源分离的目的是求得源信号的最佳估计，盲辨识的目的是求得传输通道混合矩阵。(Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever.)