Image_Spatial_Filtering

说明：  the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]: Y=φβ+ξ (7) β=〖(φ^T φ)〗^(-1) φ^T Y (8) where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.

计算机模拟matlab编程DNA程序编程(Computer simulation of Matlab programming of DNA programming)

该文件为matlab7.0的学校资料，希望对需要者能提供帮助(it is a process about matlab7.0)

产生北斗卫星伪随机码程序，利用该程序可以产生任意一颗北斗卫星的C/A码序列。(Pseudo-random code generation Beidou satellite program, use the program can generate any one Beidou satellite C/A code sequence.)

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.)

EBPSK调制解调系统，包括调制解调，抽样判决和积分判决(EBPSK Modem System)

1）了解小波分解与小波包分解概念区别与联系； 2）了解小波包的分解和重构算法积； 3）用matlab编程对信号进行小波包分解及小波能量图的绘制。 (1) to understand the wavelet decomposition and wavelet packet decomposition of differences and relations between concepts 2) to understand the wavelet packet decomposition and reconstruction algorithms of product 3) the signal using matlab programming wavelet packet decomposition and wavelet energy mapping.)

一種可用於SAR影像上的應用，並且提供使用者簡單的把圖片慮波得到更好的影像辨識(An application can be used on the SAR images, and provides users with a simple wave to get a better picture into account image recognition)

通过有限元法求解柔性支撑的铁木辛柯梁的固有频率，包含各个单元的组装，固有频率的计算！！！！！！！！！！！！！！！！！！(Through finite element method, the natural frequency of the flexible support is solved, including the assembly of each unit and the calculation of the natural frequency.)

说明：  手写字体识别，matlab平台实现，准确率达到了百分之98，bp神经网络进行反向优化(Handwritten font recognition, matlab platform implementation, accuracy rate of 98%, bp neural network for reverse optimization)