GFzhenlie

指纹识别matlab代码，包括一些指纹图片(Fingerprint recognition matlab code)

Matlab GUI 编程教程（适用于初学者）(Matlab GUI programming tutorial (for beginners))

here we will estimate delay time added to the channel and we will plot the error

speaker recognition:some matlab source codes and notes

说明：  用于描述交通流的元胞模型，能很好的仿真模拟(Used to describe the traffic flow cell model can be a good simulation)

This book is for battery specification

用rayleighchan 函数仿真时要注意最后要做相位补偿（此问题是导师发现的） 程序步骤： ~产生随机序列 ~产生瑞利信道 ~调制 ~记忆信道特征 ~信号通过信道 ~相位补偿 ~加高斯噪声 ~解调 ~计算仿真误比特率 ~计算理论误比特率 ~不同颜色画出误比特率曲线对比(Function Simulation with rayleighchan last thing to note when the phase compensation (this issue is found mentor) program steps: ~ ~ generated random sequence modulation in Rayleigh Road, ~ ~ ~ memory characteristics of the signal channel by channel ~ ~ plus Gaussian noise phase compensation ~ ~ demodulation bit error rate calculation simulation of bit error rate calculation theory ~ ~ different colors to draw curves of bit error rate)

matlab fem beam to solve displace

建立一个WS小世界的模型，返回该模型的连接矩阵(establish a WS small world model and obtain its adjency matrix)

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.)