modulationClasssify

跟踪微分器，用于自抗扰控制的过度安排作用，学习自抗扰可参考学习，谢谢(Track Differentiator)

这是一个有关语音信号中的噪声降噪的原代码，结果可能有些不好(This is one of the voice signal noise of the original noise code, the result may be some bad)

在matlab环境下，构造LDPC码的校验矩阵的各种方法，包括Gallager构造法，Mackay构造法，PEG构造法，对以后的编译码算法有很大的帮助。(In the matlab environment, various methods LDPC code parity check matrix structure, including Gallager construction law, Mackay construction law, PEG construction method, after encoding and decoding algorithms are of great help.)

基于easyX library的祝福烟花，可以给你的 TA 送去属于你自己的祝福烟花。(Blessing fireworks on easyX library, you can give your TA send your own blessings fireworks.)

系统辨识教材，个人认为很不错，希望对大家有用！(The system identification teaching material, I believe is very good, hoped that is useful to everybody!)

Robot Control with matlab

Simulation of a plane wave impinging on a dielectric cylinder using FDTD

GSM中GMSK调制信号的产生，给出接收信号眼图及系统仿真误码率，仿真效果非常好。( GSM is GMSK modulation signal generation, The received signal is given eye and BER simulation systems, Simulation of the effect is very good.)

说明：  一个双向LSTM程序 Long Short Term 网络—— 一般就叫做 LSTM ——是一种 RNN 特殊的类型，可以学习长期依赖信息。LSTM 由Hochreiter & Schmidhuber (1997)提出，并在近期被Alex Graves进行了改良和推广。在很多问题，LSTM 都取得相当巨大的成功，并得到了广泛的使用。 LSTM 通过刻意的设计来避免长期依赖问题。记住长期的信息在实践中是 LSTM 的默认行为，而非需要付出很大代价才能获得的能力！ 所有 RNN 都具有一种重复神经网络模块的链式的形式。在标准的 RNN 中，这个重复的模块只有一个非常简单的结构，例如一个 tanh 层。(A bidirectional LSTM program Long short term network, commonly known as LSTM, is a special type of RNN that can learn long-term dependent information. LSTM was proposed by Hochreiter & schmidhuber (1997) and recently improved and promoted by Alex graves. In many problems, LSTM has achieved great success and has been widely used. LSTM is designed to avoid long-term dependency. Remember that long-term information is the default behavior of LSTM in practice, not the ability to acquire it at a great cost! All RNNs have a chained form of repetitive neural network modules. In the standard RNN, this repetitive module has only a very simple structure, such as a tanh layer.)

显示关键点，返回值为关键点的坐标，尺度，方向，模值，描述符(return the (x,y),scale,derection,size)