ColonPolypsClassificationANFISTesting

半色调技术对图像进行打印和显示。半色调技术是一种基于点模式的灰度级图像打印方案。在本实验中，给出了用点模式近似表示的10个灰度级(Halftone image printing and display. Half-tone technique is a model based on gray-scale image point of printing solutions. In this experiment, given the dot pattern similar to that of the 10 gray level)

说明：  TD-SCDMA标准初稿（word版）和TD-SCDMA标准的MATLAB仿真程序(TD-SCDMA standard first draft (word version) and TD-SCDMA standard MATLAB simulation program)

薛定宇 控制系统计算机辅助设计（第二版） 源代码/源程序(On Compensation Control System Computer Aided Design (Second Edition) source code/source)

用边缘检测的方法进行参数估计，取得了较好的效果，证明是有效的方法(Edge detection method with parameter estimation, to obtain good results, proved to be an effective way to)

Lorenz 吸引子三维相空间图，这里用四阶 Runge-Kutta 法得到微方程的离散序列(Lorenz Runge-Kutta)

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.)

matlab实例程序百例2 matlab实例(Examples of procedures for 100 cases of matlab examples 2matlab)

The DSI method is basically a modified SLM method. The additional aspect of the DSI method is to add M-bits dummy sequences to the original sequence in order to reduce the PAPR. Where, the dummy bits are inserted at the end of the data sequence (y). Next, after IFFT, we select the M-DSI sequence having the lowest PAPR.

Bloch simulator. [mx,my,mz] = bloch(b1,gr,tp,t1,t2,df,dp,mode,mx,my,mz) Bloch simulation of rotations due to B1, gradient and off-resonance, including relaxation effects. At each time point, the rotation matrix and decay matrix are calculated. Simulation can simulate the steady-state if the sequence is applied repeatedly, or the magnetization starting at m0. INPUT: b1 = (Mx1) RF pulse in G. Can be complex. gr = (Mx1,2,or 3) 1,2 or 3-dimensional gradient in G/cm. tp = (Mx1) time duration of each b1 and gr point, in seconds, or 1x1 time step if constant for all points

System Hardware VxWorks kernel init