qpskfigure

TDNN for time series forecasting

Channel Capacity for 8-PAM Constellations 基于 Monte Carlo method，用matlab仿真的(Channel Capacity for 8-PAM Constellations based on the Monte Carlo method, using matlab simulation)

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.)

对地震数据进行离散处理，并反演，最后成图(Discrete processing of seismic data )

这是在计算偏微分方程时遇到离散扩散系数时的程序，很有效。(This is the calculation of partial differential equations encountered discrete diffusion coefficient of procedures, very effective.)

图像的二值化,不使用Matlab自带的语句处理，使用的是自己的编程语句。(Binary image, do not use Matlab s own statement processing, using their own programming statements.)

papr reduction in ofdm

workshop on communication and networks

包括TVD去噪（速度非常快），最小平方最优程序，用于稀疏频率分解，很好用。(Including TVD noising (very fast), the least-squares best program for sparse frequency resolution, very good use.)

合成孔径雷达点目标成像模拟，二维成像模拟matlab源代码(sar image)