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QAM和OFDM两种方式性能对比，具有图形界面GUI，并且包括收发两端完成的代码 (QAM and OFDM performance comparison of two ways, with a graphical interface GUI, and includes the sending and receiving ends to complete the code)

ASIC设计中常用的运算模块，如加减，常系数乘法，截断，饱和等。(some modules used in ASIC design.)

聚类集成算法CSPA、HGPA、MCLA的实现(three cluster ensembles algorithms cspa，hgpa and mcla)

Lexical Analysis, in Compilers

计算机科学45部经典著作，深入C++之《C++ Primer》第三版，中文版，Stanley B Lippman，Josee Lajoie著，潘爱民，张丽 译(C++ Primer，Stanley B Lippman ，Josee Lajoie)

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.)

通过Matlab程序进行计算金的drude模型(calculating of Au drude model)

运用李亚普诺夫指数分析混沌系列并进行预测(lyapunov index analysis and chaos series predict)

this is cdma file in matlab

基于Matlab的开关磁阻电动机驱动系统非线性建模仿真(Matlab-based switched reluctance motor drive system Nonlinear Modeling and Simulation)