PSOFully

3GPP标准交织器，南京邮电大学，通信工程系(3GPP standard interleaver)

自己搜集的一些变步长lms算法相关论文，希望对大家有帮助。(Some of his collection of variable step size lms algorithm related papers, we want to help.)

DCT在MATLAB中的实现 试过了，可以用，有详细讲解(DCT in the MATLAB tried to achieve that can be used, explained in detail)

用Matlab实现风车的转动，不错很实用的程序(The rotating windmill with Matlab, very good and practical procedures)

Use prec=0.01 or prec=0.005 for larger image with better resolution (and longer runtime...)

非常帅气的window32 窗口类源码，可以此为基础做进一步的开发。(A Simple Win32 Window Wrapper Class。http://www.codeproject.com/win32/win32windowwrapperclass.asp)

的风格特让他上部结构后可见很快将很快均可个黄金的身高的风格(rtdddddsvfhhhjkioiuosadsvbnn)

一种无线传感网络寿命优化算法2，研究无线传感网络非常有用(A wireless sensor network lifetime optimization algorithms in wireless sensor networks are useful)

IIR数字滤波器的设计 上学的时候一直不知道如何用C语言来编写IIR滤波器，终于找到了，上传给大家 希望能帮到大家。(IIR digital filter design when the school did not know how to use C language to write IIR filters, and finally found it, upload to hope to help you.)

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.)