Imaging-via-CS

求数字1到10000的原根，返回原根值，若无原根显示说明(Find the numbers 1 through 10,000 of the original root, return the root value, in the absence of the original description of the root shows)

A speech signal filterbank, using melscale frequency and framebanking.( 1. For speech signal can be represented as a discrete sequence of frames (or feature vectors) that can be used as the input to a speech recogniser. Important ideas and techniques that are used in the assignment are the design of a (Mel frequency scale) audio filterbank, , windowing of a continuous audio signal, spectrum analysis of the signal, filtering as multiplication in the frequency domain, the visual representation of a speech signal as a spectrogram, appreciation of the acoustic variability in real speech utterances. 2. To gain a deeper knowledge of the application of MATLAB programming to audio signal processing. 3. To gain practice in the art of writing a formal report: structure, content, style, use of diagrams, presentation etc. etc. )

说明：  实现数字滤波（刚开始接触不知道分类对不对）请见谅(Digital filter (do not know the beginning of exposure classification is correct or not), please forgive me)

abc quantity to alpha beta conversion

This a two dimensional level set code that extends quanities off the interface using a first order Godunov scheme.

移动通信系统中的维特比编码器的实现程序代码。(The codes of Viterbi Coder for mobile communication.)

This file help us how to plot CDF Function which used to plot PAPR of OFDM system. It use Matlab to run.

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.)

Red Adaline en Matlab

用m语言写的最小二乘法的算法，可以直接嵌入系统中计算，简单方便(use m language to write the method of least squares algorithm, can be directly embedded systems calculation is simple and convenient)