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Ordinary and Partial Differential Equation Routines in C, C++, Fortran, Java, Maple, and MATLAB

移动通信系统中的通信信号调制的MATLAB仿真，包括BPSKQPSK等调制方法(Mobile communication systems Communications of the MATLAB simulation of signal modulation, including BPSK QPSK modulation method, etc.)

CS仿真此仿真详细的列出了仿真的过程，并给予文字说明，是初学者的好资源！(CS simulation)

字典学习的各种方法，K-SVD,online等主要的学习算法(Various methods dictionary learning, K-SVD, online learning algorithms and other major)

CZT算法的任意点数FFT变换的浮点C程序，加入MEX借口函数，从而实现matlab的直接调用！(Floating-point C program, the CZT algorithm any point FFT transform added MEX an excuse to function, in order to achieve the matlab direct calls!)

Discrete Wavelet Transform using Daubechies Wavelet family for Stationery and non-stationery signal and test with ECG signal for compression as well. The Matlab code are very simple and detailed one to understand easily. Can change code and produce on for ones project as well. These code provided with outputs to understand what exactly we are doing and get known what DWT exactly is. I tried my best to provide simple code.

说明：  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.)

图像的二维小波分解与重构，dwt2函数和idwt2函数的应用(Two-dimensional image wavelet decomposition and reconstruction, dwt2 function and function application idwt2)

《通信系统仿真原理与无线应用》一书的matlab仿真程序("Communication System Simulation Theory and wireless applications," a book of matlab simulation program)

ss7 basics. Protocol Stack