shuikuidiaodupso

commonsonic公司的ofdm仿真源码，共享出来给大家学习，共同进步(commonsonic company ofdm simulation source code, for Members to share learning and common progress)

Turboedit算法用于探测GSP载波相位观测值中的周跳，简要的matlab代码实现(detect cycle slip by Turboedit)

Real time simulink model for IPMSM with PI controller. Suitable for DS1104 DSP board.

Simulation of basic circuit eletric

MATLAB实现算术编码，一种压缩编码，信息论与编码理论(MATLAB arithmetic coding to achieve)

说明：  《数字图像处理与机器视觉:Visual C++与Matlab实现》4 特征提取,图像识别初步,人工神经网络,基于ANN的数字字符识别系统(&quot Digital image processing and machine vision: Visual C++ and Matlab to achieve&quot four feature extraction, image recognition initially, artificial neural network, ANN based digital character recognition system)

实验要求：在一张图上绘制三种二进制信号（即反极性信号、开关信号和正交信号）的误码率。这三种信号可以在几何上用“信号空间”中的点来表示。在反极性信号情况下，信号是±s(t)，每个都有能量E。开关信号也是一维信号，所以两个信号为0和s(t)，正交信号我们上课已讲过。要求信噪比为0：12dB。(Experimental requirements: to draw a diagram of three binary signals (i.e., reverse polarity signal, the switching signal and quadrature signal) BER. These three signals can be in the geometric point in the signal space. In the reverse polarity signal case, the signal is s (t), each have energy E. The switching signal is also a one-dimensional signal, so the two signals is 0 and s (t), the quadrature signals teach us have talked. Signal-to-noise ratio to 0:12 dB.)

医学图像配准工具箱，对用互信息图像配准有帮助(Medical image registration toolbox for image registration using mutual information helpful)

进行了3D MIMO下多用户的系统容量和Taylor容量的对比(Compare the multi-user system capacity in 3D MIMO)

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.)