pls

MATLAB and VC 联合编程的实现(MATLAB and VC lianhe biancheng shixian de lizi)

Matlab guide for digital signal processing.

C++ Project. Demonstration of object orientated programming with minesweeper game implementation long time

这是基于matlab的电路仿真,程序是自己改写的,还不多,希望大家多多交流.(This is based on Matlab circuit simulation, the process is write, and do not, We hope to conduct more exchanges.)

实现语音断点检测的程序，有静音处理和噪声去除，已编译通过。(Voice breakpoint detection program, mute processing and noise removal, compiled by.)

各种干扰信号,包括噪声调频干扰,噪声调相干扰,噪声调幅干扰,射频噪声干扰(A variety of interfering signals, including noise FM interference, noise, phase modulation, interference, noise AM interference, RF noise)

计算Rotman_lens结构中馈电点的位置的matlab程式(calculate the position of feed point in rotman_lens)

基于matlab的商场的季节性线性回归销售图源代码(Based on matlab mall seasonal sales source code)

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.)

ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING MATLAB CODE