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这是一个产生混沌时间序列Duffing2吸引子的程序(Duffing2)

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

比较卡尔曼滤波中状态方程的作用的ＭＡＴＬＡＢ源码(compared Kalman filtering equation of state of the role of MATLAB source)

Harris corner extraction matlab code，摄像头标定时采用的角点检测方法，用于提取角点像素坐标。(Harris corner extraction matlab code, camera calibration using corner detection method for extracting corner pixel coordinates.)

Long Term Evolution LTE block interleaver and deinterleaver

Calculation wc and kd on a desirable stock of stability of a contour

说明：  MIMO仿真所需的小程序，里面包括注水算法的子程序(MIMO simulation for a small program, which includes water injection method of subroutine)

the recovery of the 2D SAR image with l1-norm minimization

小数量化程序,中间的位数是可以调节的，这个所给的是32位的实数浮点数据转变为二进制数(the procedure of quantification for decimals)

一本讲模型预测控制算法的教材，书中提供m语言源代码(A model predictive control algorithm good teaching, the book m language source code!)