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music算法的M文件，实现经典MUSIC的Matlab仿真(M-algorithm music files, the realization of the Matlab simulation of the classic MUSIC)

image edge detect funtion for matlab and example image POSTECH

移动通信--无线楼宇数据传输系统的仿真.分析915M的楼宇通信特性，建立楼宇数据通信系统的仿真模型，采用无线收发芯片NRF905，依据其性能参数来判断楼宇内两点之间能否进行数据传输。(mobile communications-- wireless data transmission system building simulation. Analysis of cellphone communications characteristics of the buildings, establish premises data communications system simulation models, the use of wireless transceiver chip NRF905. based on its performance parameters to judge between the two buildings can for data transfer.)

一种智能优化算法-粒子群算法的matlab源程序(particle swarm)

Bit Error Rate simulation and theorique with SNR.

有关MATLAB软件的超详细介绍 适合初学者(For details of MATLAB software for beginners super)

GUI CODIGO DE LINEA INTRODUCIENDO UN CARACTER

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.)

the plot of EEG power spectral values

kalman滤波程序，非常适合研究该领域的朋友，欢迎共享(kalman filtering procedures, is very suitable for research in this field, welcome to share the)